Intelligent Orchestration Systems for Energy and Power Management of Heterogeneous Energy-Related Systems and Devices

ABSTRACT

Disclosed herein are AI-based platforms for enabling intelligent orchestration and management of power and energy. In various embodiments, a set of edge devices includes a set of artificial intelligence systems that are configured to process data handled by the edge devices and determine, based on the data, a mix of energy generation, storage, delivery and/or consumption characteristics for a set of systems that are in local communication with the edge devices and to output a data set that represents the constituent proportions of the mix. In some embodiments, the output data set indicates a fraction of energy generated by an energy grid and a fraction of energy generated by a set of distributed energy resources that operate independently of the energy grid. In some embodiments, the output data set indicates a fraction of energy generated by renewable energy resources and a fraction of energy generated by nonrenewable resources.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of PCT Application No.PCT/US22/50932 filed Nov. 23, 2022, which claims the benefit of U.S.Provisional Application Nos. 63/375,225 filed Sep. 10, 2022, 63/302,016filed Jan. 21, 2022, 63/299,727 filed Jan. 14, 2022, 63/291,311 filedDec. 17, 2021, and 63/282,510 filed Nov. 23, 2021.

This application is a continuation of PCT Application No. PCT/US22/50924filed Nov. 23, 2022, which claims the benefit of U.S. ProvisionalApplication Nos. 63/375,225 filed Sep. 10, 2022, 63/302,016 filed Jan.21, 2022, 63/299,727 filed Jan. 14, 2022, 63/291,311 filed Dec. 17,2021, and 63/282,510 filed Nov. 23, 2021.

The entire disclosures of the above applications are incorporated byreference.

BACKGROUND

Energy remains a critical factor in the world economy and is undergoingan evolution and transformation, involving changes in energy generation,storage, planning, demand management, consumption and delivery systemsand processes. These changes are enabled by the development andconvergence of numerous diverse technologies, including moredistributed, modular, mobile and/or portable energy generation andstorage technologies that will make the energy market much moredecentralized and localized, as well as a range of technologies thatwill facilitate management of energy in a more decentralized system,including edge and Internet of Things networking technologies, advancedcomputation and artificial intelligence technologies, transactionenablement technologies (such as blockchains, distributed ledgers andsmart contracts) and others. The convergence of these more decentralizedenergy technologies with these networking, computation and intelligencetechnologies is referred to herein as the “energy edge.”

The energy market is expected to evolve and transform over the next fewdecades from a highly centralized model that relies on fossil fuels anda managed electrical grid to a much more distributed and decentralizedmodel that involves many more localized generation, storage, andconsumption systems. During that transition, a hybrid system will likelypersist for many years in which the conventional grid becomes moreintelligent, and in which distributed systems will play a growing role.A need exists for a platform that facilitates management and improvementof legacy infrastructure in coordination with distributed systems.

SUMMARY

An AI-based energy edge platform is provided herein with a wide range offeatures, components and capabilities for management and improvement oflegacy infrastructure and coordination with distributed systems tosupport important use cases for a range of enterprises. The platform mayincorporate emerging technologies to enable ecosystem and individualenergy edge node efficiencies, agility, engagement, and profitability.Embodiments may be guided by, and in some cases integrated with,methodologies and systems that are used to forecast, plan for, andmanage the demand and utilization of energy in greater distributedenvironments. Embodiments may use AI, and AI enablers such as IoT, whichmay be deployed in vastly denser data environments (reflecting theproliferation of smart energy systems and of sensors in the IoT), aswell as technologies that filter, process, and move data moreeffectively across communication networks. Embodiments of the platformmay leverage energy market connection, communication, and transactionenablement platforms. Embodiments may employ intelligent provisioning,data aggregation, and analytics. Among many use cases the platform mayenable improvements in the optimization of energy generation, storage,delivery and/or enterprise consumption in operations (e.g., buildings,data centers, and factories, among many others), the integration and useof new power generation and energy storage technologies and assets(distributed energy resources, or “DERs”), the optimization of energyutilization across existing networks and the digitalization of existinginfrastructure and supporting systems.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will become more fully understood from thedetailed description and the accompanying drawings.

FIG. 1 is a schematic diagram that presents an introduction of platformand main elements, according to some embodiments.

FIGS. 2A and 2B are schematic diagrams that present an introduction ofmain subsystems of a major ecosystem, according to some embodiments.

FIG. 3 is a schematic diagram that presents more detail on distributedenergy generation systems, according to some embodiments.

FIG. 4 is a schematic diagram that presents more detail on dataresources, according to some embodiments.

FIG. 5 is a schematic diagram that presents more detail on configuredenergy edge stakeholders, according to some embodiments.

FIG. 6 is a schematic diagram that presents more detail on intelligenceenablement systems, according to some embodiments.

FIG. 7 is a schematic diagram that presents more detail on AI-basedenergy orchestration, according to some embodiments.

FIG. 8 is a schematic diagram that presents more detail on configurabledata and intelligence, according to some embodiments.

FIG. 9 is a schematic diagram that presents a dual-process learningfunction of a dual-process artificial neural network, according to someembodiments.

FIG. 10 through FIG. 37 are schematic diagrams of embodiments of neuralnet systems that may connect to, be integrated in, and be accessible bythe platform for enabling intelligent transactions including onesinvolving expert systems, self-organization, machine learning,artificial intelligence and including neural net systems trained forpattern recognition, for classification of one or more parameters,characteristics, or phenomena, for support of autonomous control, andother purposes in accordance with embodiments of the present disclosure.

FIG. 38 is a schematic view of an exemplary embodiment of a quantumcomputing service according to some embodiments of the presentdisclosure.

FIG. 39 illustrates quantum computing service request handling accordingto some embodiments of the present disclosure.

FIG. 40 is a diagrammatic view of a thalamus service and how itcoordinates within the modules in accordance with the presentdisclosure.

FIG. 41 is another diagrammatic view of a thalamus service and how itcoordinates within the modules in accordance with the presentdisclosure.

DETAILED DESCRIPTION FIG. 1: Introduction of Platform and Main Elements

In embodiments, provided herein is an AI-based energy edge platform 102,referred to herein for convenience in some cases as simply the platform102, including a set of systems, subsystems, applications, processes,methods, modules, services, layers, devices, components, machines,products, sub-systems, interfaces, connections, and other elementsworking in coordination to enable intelligent, and in some casesautonomous or semi-autonomous, orchestration and management of power andenergy in a variety of ecosystems and environments that includedistributed entities (referred to herein in some cases as “distributedenergy resources” or “DERs”) and other energy resources and systems thatgenerate, store, consume, and/or transport energy and that include IoT,edge and other devices and systems that process data in connection withthe DERs and other energy resources and that can be used to inform,analyze, control, optimize, forecast, and otherwise assist in theorchestration of the distributed energy resources and other energyresources.

In embodiments, the platform 102 enables a set of configured stakeholderenergy edge solutions 108, with a wide range of functions, applications,capabilities, and uses that may be accomplished, without limitation, byusing or orchestrating a set of advanced energy resources and systems104, including DERs and others. The configured stakeholder energy edgesolution 108 may integrate, for example, domain-specific stakeholderdata, such as proprietary data sets that are generated in connectionwith enterprise operations, analysis and/or strategy, real-time datafrom stakeholder assets (such as collected by IoT and edge deviceslocated in proximity to the assets and operations of the stakeholder),stakeholder-specific energy resources and systems 104 (such as availableenergy generation, storage, or distribution systems that may bepositioned at stakeholder locations to augment or substitute for anelectrical grid), and the like into a solution that meets thestakeholder's energy needs and capabilities, including baseline, period,and peak energy needs to conduct operations such as large-scale dataprocessing, transportation, production of goods and materials, resourceextraction and processing, heating and cooling, and many others.

In embodiments, the AI-based energy edge platform 102 (and/or elementsthereof) and/or the set of configured stakeholder energy edge solutions108 may take data from, provide data to and/or exchange data with a setof data resources for energy edge orchestration 110.

The AI-based energy edge platform 102 may include, integrate with,exchange data with and/or otherwise link to a set of intelligenceenablement systems 112, a set of AI-based energy orchestration,optimization, and automation systems 114 and a set of configurable dataand intelligence modules and services 118.

The set of intelligence enablement systems 112 may include a set ofintelligent data layers 130, a set of distributed ledger and smartcontract systems 132, a set of adaptive energy digital twin systems 134,and/or a set of energy simulation systems 136.

The set of AI-based energy orchestration, optimization, and automationsystems 114 may include a set of energy generation orchestration systems138, a set of energy consumption orchestration systems 140, a set ofenergy marketplace orchestration systems 146, a set of energy deliveryorchestration systems 147, and a set of energy storage orchestrationsystems 142.

The set of configurable data and intelligence modules and services 118may include a set of energy transaction enablement systems 144, a set ofstakeholder energy digital twins 148 and a set of data integratedmicroservices 150 that may enable or contribute to enablement of the setof configured stakeholder energy edge solutions 108.

The AI-based energy edge platform 102 may include, integrate with, linkto, exchange data with, be governed by, take inputs from, and/or provideoutputs to one or more artificial intelligence (AI) systems, which mayinclude models, rule-based systems, expert systems, neural networks,deep learning systems, supervised learning systems, robotic processautomation systems, natural language processing systems, intelligentagent systems, self-optimizing and self-organizing systems, and othersas described throughout this disclosure and in the documentsincorporated by reference herein. Except where context specificallyindicates otherwise, references to AI, or to one or more examples of AI,should be understood to encompass these various alternative methods andsystems; for example, without limitation, an AI system described forenabling any of a wide variety of functions, capabilities and solutionsdescribed herein (such as optimization, autonomous operation,prediction, control, orchestration, or the like) should be understood tobe capable of implementation by operation on a model or rule set; bytraining on a training data set of human tag, labels, or the like; bytraining on a training data set of human interactions (e.g., humaninteractions with software interfaces or hardware systems); by trainingon a training data set of outcomes; by training on an AI-generatedtraining data set (e.g., where a full training data set is generated byAI from a seed training data set); by supervised learning; bysemi-supervised learning; by deep learning; or the like. For any givenfunction or capability that is described herein, neural networks ofvarious types may be used, including any of the types described hereinor in the documents incorporated by reference, and, in embodiments, ahybrid set of neural networks may be selected such that within the set aneural network type that is more favorable for performing each elementof a multi-function or multi-capability system or method is implemented.As one example among many, a deep learning, or black box, system may usea gated recurrent neural network for a function like languagetranslation for an intelligent agent, where the underlying mechanisms ofAI operation need not be understood as long as outcomes are favorablyperceived by users, while a more transparent model or system and asimpler neural network may be used for a system for automatedgovernance, where a greater understanding of how inputs are translatedto outputs may be needed to comply with regulations or policies.

AI-Based Energy Orchestration, Optimization and Automation Systems

In embodiments, the platform may employ demand forecasting, includingautomated forecasting by artificial intelligence or by taking a datastream of forecast information from a third party. Among other things,forecasting demand helps inform site selection and intelligently plannednetwork expansion. In embodiments, machine learning algorithms maygenerate multiple forecasts—such as about weather, prices, solargeneration, energy demand, and other factors—and analyze how energyassets can best capture or generate value at different times and/orlocations.

In embodiments, AI-based energy orchestration, optimization, andautomation systems 114 may enable energy pattern optimization, such asby analyzing building or other operational energy usage and seeking toreshape patterns for optimization (e.g., by modeling demand response tovarious stimuli).

The AI-based energy orchestration, optimization, and automation systems114 may be enabled by the set of intelligence enablement systems 112that provide functions and capabilities that support a range ofapplications and use cases.

Subsystems and Modules of Intelligence Enablement Systems IntelligentData Layers

The intelligence enablement systems 112 may include a set of intelligentdata layers 130, such as a set of services (including microservices),APIs, interfaces, modules, applications, programs, and the like whichmay consume any of the data entities and types described throughout thisdisclosure and undertake a wide range of processing functions, such asextraction, cleansing, normalization, calculation, transformation,loading, batch processing, streaming, filtering, routing, parsing,converting, pattern recognition, content recognition, objectrecognition, and others. Through a set of interfaces, a user of theplatform 102 may configure the intelligent data layers 130 or outputsthereof to meet internal platform needs and/or to enable furtherconfiguration, such as for the stakeholder energy edge solutions 108.The intelligent data layers 130, intelligence enablement systems 112more generally, and/or the configurable data and intelligence modulesand services 118 may access data from various sources throughout theplatform 102 and, in embodiments, may operate from the set of shareddata resources 130, which may be contained in a centralized databaseand/or in a set of distributed databases, or which may consist of a setof distributed or decentralized data sources, such as IoT or edgedevices that produce energy-relevant event logs or streams. Theintelligent data layers 130 may be configured for a wide range ofenergy-relevant tasks, such as prediction/forecasting of energyconsumption, generation, storage or distribution parameters (e.g., atthe level of individual devices, subsystems, systems, machines, orfleets); optimization of energy generation, storage, distribution orconsumption (also at various levels of optimization); automateddiscovery, configuration and/or execution of energy transactions(including microtransactions and/or larger transactions in spot andfutures markets as well as in peer-to-peer groups or single counterpartytransactions); monitoring and tracking of parameters and attributes ofenergy consumption, generation, distribution and/or storage (e.g.,baseline levels, volatility, periodic patterns, episodic events, peaklevels, and the like); monitoring and tracking of energy-relatedparameters and attributes (e.g., pollution, carbon production, renewableenergy credits, production of waste heat, and others); automatedgeneration of energy-related alerts, recommendations and other content(e.g., messaging to prompt or promote favorable user behavior); and manyothers.

Distributed Ledger and Smart Contract Systems

Energy edge intelligence enablement systems 112 may include a smartcontract system 132 for handling a set of smart contracts, each of whichmay optionally operate on a set of blockchain-based distributed ledgers.Each of the smart contracts may operate on data stored in the set ofdistributed ledgers or blockchains, such as to record energy-relatedtransactional events, such as energy purchases and sales (in spot,forward and peer-to-peer markets, as well as direct counterpartytransactions), relevant service charges and the like; transactionrelevant energy events, such as consumption, generation, distributionand/or storage events, and other transaction-relevant events oftenassociated with energy, such as carbon production or abatement events,renewable energy credit events, pollution production or abatementevents, and the like. The set of smart contracts handled by the smartcontract system 132 may consume as a set of inputs any of the data typesand entities described throughout this disclosure, undertake a set ofcalculations (optionally configured in a flow that takes inputs fromdisparate systems in a multi-step transaction), and provide a set ofoutputs that enable completion of a transaction, reporting (optionallyrecorded on a set of distributed ledgers), and the like. Energytransactional enablement systems 144 may be enabled or augmented byartificial intelligence, including to autonomously discover, configure,and execute transactions according to a strategy and/or to provideautomation or semi-automation of transactions based on training and/orsupervision by a set of transaction experts. In embodiments, the smartcontract systems 132 may be used by the energy transactional enablementsystems 144 (described elsewhere in this disclosure) to configuretransactional solutions.

Adaptive Energy Digital Twin Systems

Any entity, analytic results, output of artificial intelligence, state,operating condition, or other feature noted throughout this disclosuremay, in embodiments, be presented in a digital twin, such as theadaptive energy digital twin 134, which is widely applicable, and/or thestakeholder energy digital twin 148, which is configured for the needsof a particular stakeholder or stakeholder solution. The adaptive energydigital twin 134 may, for example, provide a visual or analyticindicator of energy consumption by a set of machines, a group offactories, a fleet of vehicles, or the like; a subset of the same (e.g.,to compare energy parameters by each of a set of similar machines toidentify out-of-range behavior); and many other aspects. A digital twinmay be adaptive, such as to filter, highlight, or otherwise adjust datapresented based on real-time conditions, such as changes in energycosts, changes in operating behavior, or the like.

Energy Simulation Systems

In embodiments, a set of energy simulation systems 136 is provided, suchas to develop and evaluate detailed simulations of energy generation,demand response and charge management, including a simulationenvironment that simulates the outcomes of use of various algorithmsthat may govern generation across various generations assets,consumption by devices and systems that demand energy, and storage ofenergy. Data can be used to simulate the interaction of non-controllableloads and optimized charging processes, among other use cases. Thesimulation environment may provide output to, integrate with, or sharedata with the set of advanced energy digital twin systems 134.

In embodiments, as more enterprises embrace hybrid infrastructure,uptime is becoming more complex, requiring backup and failoverstrategies that span cloud, colocation, on-premises facilities, and edgeinfrastructure. This may include AI-based algorithms for automaticallymanaging energy for devices and systems in such devices. For example,artificial intelligence may enable autonomous data center cooling andindustrial control. In embodiments, DERs 128 may be integrated into orwith, for example, AI-driven computing infrastructure, smart PDUs, UPSsystems, energy-enabled air flow management systems, and HVAC systems,among others.

Introduction of Main Subsystems and Modules of AI-Based EnergyOrchestration, Optimization, and Automation Systems

The set of AI-based energy orchestration, optimization, and automationsystems 114 may include the set of energy generation orchestrationsystems 138, the set of energy consumption orchestration systems 140,the set of energy storage orchestration systems 142, the set of energymarketplace orchestration systems 146 and the set of energy deliveryorchestration systems 147, among others. For example, the energydelivery orchestration systems 147 may enable orchestration of thedelivery of energy to a point of consumption, such as by fixedtransmission lines, wireless energy transmission, delivery of fuel,delivery of stored energy (e.g., chemical or nuclear batteries), or thelike, and may involve autonomously optimizing the mix of energy typesamong the foregoing available resources based on various factors, suchas location (e.g., based on distance from the grid), purpose or type ofconsumption (e.g., whether there is a need for very high peak energydelivery, such as for power-intensive production processes), and thelike.

Configurable Data and Intelligence Modules and Services

In embodiments, the platform 102 may include a set of configurable dataand intelligence modules and services 118. These may include energytransaction enablement systems 144, stakeholder energy digital twins148, energy-related data integrated microservices 150, and others. Eachmodule or service (optionally configured in a microservicesarchitecture) may exchange data with the various data resources 110 inorder to provide a relevant output, such as to support a set of internalfunctions or capabilities of the platform 102 and/or to support a set offunctions or capabilities of one or more of the configured stakeholderenergy edge solutions 108. As one example among many, a service may beconfigured to take event data from an IoT device that has cameras orsensors that monitor a generator and integrate it with weather data froma public data resource 162 to provide a weather-correlated timeline ofenergy generation data for the generator, which in turn may be consumedby a stakeholder energy edge solution 108, such as to assist withforecasting day-ahead energy generation by the generator based on aday-ahead weather forecast. A wide range of such configured data andintelligence modules and services 118 may be enabled by the platform102, representing, for example, various outputs that consist of thefusion or combination of the wide range of energy edge data sourceshandled by the platform, higher-level analytic outputs resulting fromexpert analysis of data, forecasts and predictions based on patterns ofdata, automation and control outputs, and many others.

Energy Transaction Enablement Systems

Configurable data and intelligence modules and services 118 may includeenergy transaction enablement systems 144. Transaction enablementsystems 144 may include a set of smart contracts, which may operate ondata stored in a set of distributed ledgers or blockchains, such as torecord energy-related transactional events, such as energy purchases andsales (in spot, forward and peer-to-peer markets, as well as directcounterparty transactions) and relevant service charges; transactionrelevant energy events, such as consumption, generation, distributionand/or storage events, and other transaction-relevant events oftenassociated with energy, such as carbon production or abatement events,renewable energy credit events, pollution production or abatementevents, and the like. The set of smart contracts may consume as a set ofinputs any of the data types and entities described throughout thisdisclosure, undertake a set of calculations (optionally configured in aflow that takes inputs from disparate systems in a multi-steptransaction), and provide a set of outputs that enable completion of atransaction, reporting (optionally recorded on a set of distributedledgers), and the like. Energy transactional enablement systems 144 maybe enabled or augmented by artificial intelligence, including toautonomously discover, configure, and execute transactions according toa strategy and/or to provide automation or semi-automation oftransactions based on training and/or supervision by a set oftransaction experts. Autonomy and/or automation (supervised orsemi-supervised) may be enabled by robotic process automation, such asby training a set of intelligent agents on transactional discovery,configuration, or execution interactions of a set of transactionalexperts with transaction-enabling systems (such as software systems usedto configure and execute energy trading activities).

As energy is increasingly produced and consumed in local, decentralizedmarkets, the energy market is likely to follow patterns of otherpeer-to-peer or shared economy markets, such as ride sharing, apartmentsharing and used goods markets. Technology enables the bypassing oftop-down or centralized energy supply and enables operators to createplatforms that can manage and monetize spare capacity, such as throughthe leasing and trading of assets and outputs.

As more distributed or peer-to-peer transactive energy markets develop,the platform 102 may include systems or link to, integrate with, orenable other platforms that facilitate P2P trading, wholesale contracts,renewable energy certificate (REC) tracking, and broader distributedenergy provisioning, payment management and other transaction elements.In embodiments, the foregoing may use blockchain, distributed ledgerand/or smart contract systems 132.

In embodiments, with increased transparency, choice, and flexibility,consumers will be able to participate actively in energy markets, bygenerating, storing, and selling, as well as consuming electricity.

In embodiments, transactional elements may be configured by energytransaction enablement systems 144 to optimize energy generation,storage, or consumption, such as utility time of use charges. Shiftingenergy demand away from high-priced time periods with IoT-basedplatforms that can identify periods where energy costs are the leastexpensive.

Stakeholder Energy Digital Twins

The configurable data and intelligence modules and services 118 mayinclude one or more stakeholder energy digital twins 148, which may, inembodiments, include set of digital twins that are configured torepresent a set of stakeholder entities that are relevant to energy,including stakeholder-owned and stakeholder-operated energy generationresources, energy distribution resources, and/or energy distributionresources (including representing them by type, such as indicatingrenewable energy systems, carbon-producing systems, and others);stakeholder information technology and networking infrastructureentities (e.g., edge and IoT devices and systems, networking systems,data centers, cloud data systems, on premises information technologysystems, and the like); energy-intensive stakeholder productionfacilities, such as machines and systems used in manufacturing;stakeholder transportation systems; market conditions (e.g., relating tocurrent and forward market pricing for energy, for the stakeholder'ssupply chain, for the stakeholders product and services, and the like),and others. The digital twins 148 may provide real-time information,such as provided sensor data from IoT and edge devices, event logs, andother information streams, about status, operating conditions, and thelike, particularly relating to energy consumption, generation, storage,and or distribution.

The stakeholder energy digital twin 148 may provide a visual, real-timeview of the impact of energy on all aspects of an enterprise. A digitaltwin may be role-based, such as providing visual and analytic indicatorsthat are suitable for the role of the user, such as financial reportinginformation for a CFO; operating parameter information for a power plantmanager; and energy market information for an energy trader.

Data Integrated Microservices

The configurable data and intelligence modules and services 118 mayinclude configurable data integrated microservices 150, such asorganized in a service-oriented architecture, such that variousmicroservices can be grouped in series, in parallel, or in more complexflows to create higher-level, more complex services that each provide adefined set of outputs by processing a defined set of outputs, such asto enable a particular stakeholder solution 108 or to facilitateAI-based orchestration, optimization and/or automation systems 114. Theconfigurable data and intelligence modules and services 118 may, withoutlimitation, be configured from various functions and capabilities of theintelligent data layers 130, which in turn operate on various dataresources for energy edge orchestration 110 and/or internal event logs,outputs, data streams and the like of the platform 102.

FIGS. 2A-2B: Introduction of Main Subsystems of Major EcosystemComponents Data Resources for Energy Edge Orchestration

Referring to FIG. 2A, the data resources for energy edge orchestration110 may include a set of Edge and IoT Networking Systems 160, a set ofPublic data resources 162, and/or a set of Enterprise data resources168, which in embodiments may use or be enabled by an Adaptive EnergyData Pipeline 164 that automatically handles data processing, filtering,compression, storage, routing, transport, error correction, security,extraction, transformation, loading, normalization, cleansing and/orother data handling capabilities involved in the transport of data overa network or communication system. This may include adapting one or moreof these aspects of data handling based on data content (e.g., by packetinspection or other mechanisms for understanding the same), based onnetwork conditions (e.g., congestion, delays/latency, packet loss, errorrates, cost of transport, quality of service (QoS), or the like), basedon context of usage (e.g., based on user, system, use case, application,or the like, including based on prioritization of the same), based onmarket factors (e.g., price or cost factors), based on userconfiguration, or other factors, as well as based on variouscombinations of the same. For example, among many others, a least-costroute may be automatically selected for data that relates to managementof a low-priority use of energy, such as heating a swimming pool, whilea fastest or highest-QoS route may be selected for data that supports aprioritized use or energy, such as support of critical healthcareinfrastructure.

Referring to FIG. 2B, the platform 102 and orchestration may include,integrate, link to, integrate with, use, create, or otherwise handle, awide range of data resources for the advanced energy resources andsystems 104, the configured stakeholder energy edge solutions 108,and/or the energy edge orchestration 110. In embodiments, elements ofthe advanced energy resources and systems 104, the configuredstakeholder energy edge solutions 108, and/or the energy edgeorchestration 110 may be the same as, similar to, or different fromcorresponding elements shown in FIG. 1 . The data resources 110 mayinclude separate databases, distributed databases, and/or federated dataresources, among many others.

Edge and IoT Networking Systems

A wide range of energy-related data may be collected and processed(including by artificial intelligence services and other capabilities),and control instructions may be handled, by a set of edge and IoTnetworking systems 160, such as ones integrated into devices, componentsor systems, ones located in IoT devices and systems, ones located inedge devices and systems, or the like, such as where the foregoing arelocated in or around energy-related entities, such as ones used byconsumers or enterprises, such as ones involved in energy generation,storage, delivery or use. These include any of the wide range ofsoftware, data and networking systems described herein.

Public Data Resources

In embodiments, the platform 102 may track various public data resources162, such as weather data. Weather conditions can impact energy use,particularly as they relate to HVAC systems. Collecting, compiling, andanalyzing weather data in connection with other building informationallows building managers to be proactive about HVAC energy consumption.A wide range of public data resources 162 may include satellite data,demographic and psychographic data, population data, census data, marketdata, website data, ecommerce data, and many other types.

Enterprise Data Resources

Enterprise data resources 168 may include a wide range of enterpriseresources, such as enterprise resource planning data, sales andmarketing data, financial planning data, accounting data, tax data,customer relationship management data, demand planning data, supplychain data, procurement data, pricing data, customer data, product data,operating data, and many others.

Subsystems and Modules of Advanced Energy Resources and Systems

In embodiments, the advanced energy resources and systems 104 mayinclude distributed energy resources 128, or “DERs” 128. Moredecentralized energy resources will mean that more individuals,networked groups, and energy communities will be capable of generatingand sharing their own energy and coordinating systems to achieveultimate efficacy. The DER 128 may be a small- or medium-scale unit ofpower generation and/or storage that operates locally and may beconnected to a larger power grid at the distribution level. That is, theDER systems 128 may be either connected to the local electric power gridor isolated from the grid in stand-alone applications.

Transformed Energy Infrastructure

The advanced energy resources and systems 104 orchestrated by theplatform 102 may include transformed energy infrastructure 120. Theenergy edge will involve increasing digitalization of generation,transmission, substation, and distribution assets, which in turn willshape the operations, maintenance, and expansion of legacy gridinfrastructure. In embodiments, a set of transformed energyinfrastructure systems 120 may be integrated with or linked to theplatform 102. The transition to improved infrastructure may includemoving from SCADA systems and other existing control, automation, andmonitoring systems to IoT platforms with advanced capabilities.

In embodiments, new assets added to or coordinated with the grid (e.g.,DERs 128) may be compatible with existing infrastructure to maintainvoltage, frequency, and phase synchronization.

Any improvements to legacy grid assets, new grid-connected equipment,and supporting systems may, in embodiments, comply with regulatorystandards from NERC, FERC, NIST, and other relevant authorities;positively impact the reliability of the grid; reduce the grid'ssusceptibility to cyberattacks and other security threats; increase theability of the grid to adapt to extensive bi-directional flow of energy(i.e., DER proliferation); and offer interoperability with technologiesthat improve the efficiency of the grid (i.e., by providing andpromoting demand response, reducing grid congestion, etc.).

Digitalization of legacy grid assets may relate to assets used forgeneration, transmission, storage, distribution or the like, includingpower stations, substations, transmission wires, and others.

In embodiments, in order to maintain and improve existing energyinfrastructure, the platform 102 may include various capabilities,including fully integrated predictive maintenance across utility-ownedassets (i.e., generation, transmission, substations, and distribution);smart (AI/ML-Based) Outage Detection and Response; and/or Smart(AI/ML-Based) Load Forecasting, Including optional integration of theDERs 128 with the existing grid.

In embodiments, power grid maintenance may be provided. With proactivemaintenance, utilities can accurately detect defects and reduceunplanned outages to better serve customers. AI systems, deployed withIoT and/or edge computing, can help monitor energy assets and reducemaintenance costs.

Digitized Resources

In embodiments, the platform 102 may take advantage of the digitaltransformation of a wide range of digitized resources. Machines arebecoming smarter, and software intelligence is being embedded into everyaspect of a business, helping drive new levels of operational efficiencyand innovation. Also, digital transformation is ongoing, involvingincreasing presence of smart devices and systems that are capable ofdata processing and communication, nearly ubiquitous sensors in edge,IoT and other devices, and generation of large, dense streams of data,all of which provide opportunities for increased intelligence,automation, optimization, and agility, as information flows continuouslybetween the physical and digital world. Such devices and systems demandlarge amounts of energy. Data centers, for example, consume massiveamounts of energy, and edge and IoT devices may be deployed in off-gridenvironments that require alternative forms of generation, storage, ormobility of energy. In embodiments, a set of digitized resources may beintegrated, accessed, or used for optimization of energy for compute,storage, and other resources in data centers and at the edge, amongother places. In embodiments, as more and more devices are embedded withsensors and controls, information can flow continuously between thephysical and digital worlds as machines ‘talk’ to each other. Productscan be tracked from source to customer, or while they are in use,enabling fast responses to internal and external changes. Those taskedwith managing or regulating such systems can gain detailed data fromthese devices to optimize the operation of the entire process. Thistrend turns big data into smart data, enabling significant cost- andprocess efficiencies.

In embodiments, advances in digital technologies enable a level ofmonitoring and operational performance that was not previously possible.Thanks to sensors and other smart assets, a service provider can collecta wide range of data across multiple parameters, monitoring inreal-time, 24 hours a day.

In embodiments, the DERs 128 will be integrated into computationalnetworks and infrastructure devices and systems, augmenting the existingpower grid and serving to decrease costs and improve reliability.

Mobile Energy Resources

In embodiments, DERs may be integrated into mobile energy resources 124,such as electric vehicles (EVs) and their chargingnetworks/infrastructure, thereby augmenting the existing power grid andserving to decrease costs and improve reliability. Given the rise of EVs(of all types) charging infrastructure and vehicle charging plans willneed to be optimized to match supply and demand. Also, growingelectricity demand and development of EV infrastructure will requireoptimization using edge and other related technologies such as IoT.Electric vehicle charging may be integrated into decentralizedinfrastructure and may even be used as the DER 128 by adding to thegrid, such as through two-way charging stations, or by powering anothersystem locally. Vehicle power electronic systems and batteries canbenefit the power grid by providing system and grid services. Excessenergy can be stored in the vehicles as needed and discharged whenrequired. This flexibility option not only avoids expensive load peaksduring times of short-term, high-energy demand but also increases theshare of renewable energy use.

In embodiments, in order to universally integrate electric vehicles andcharging infrastructure into a distribution network, coordination withvarious other standardized communication protocols is needed. TheAI-based energy edge platform 102 may include, integrate and/or link toa set of communication protocols that enable management, provisioning,governance, control or the like of energy edge devices and systems usingsuch protocols.

Configured Stakeholder Energy Edge Solutions

The set of configured stakeholder energy edge solutions 108 may includea set of Mobility Demand Solutions 152, a set of Enterprise OptimizationSolutions 154, a set of Energy Provisioning and Governance Solutions 156and/or a set of Localized Production Solutions 158, among others, thatuse various advanced energy resources and systems 104 and/or variousconfigurable data and intelligence modules and services 118 to enablebenefits to particular stakeholders, such as private enterprises,non-governmental organizations, independent service organizations,governmental organizations, and others. All such solutions may leverageedge intelligence, such as using data collected from onboard orintegrated sensors, IoT systems, and edge devices that are located inproximity to entities that generate, store, deliver and/or use energy tofeed models, expert systems, analytic systems, data services,intelligent agents, robotic process automation systems, and otherartificial intelligence systems into order to facilitate a solution fora particular stakeholder needs.

Enterprise Optimization Solutions

In embodiments, the DERs 128 will be integrated with or into enterprisesand shared resources, augmenting the existing power grid and serving todecrease costs and improve reliability. Increasing levels ofdigitalization will help integrate activities and facilitate new ways ofoptimizing energy in buildings/operations, and across campuses andenterprises. In embodiments, this may enable increasing the operationalbottom line of a for-profit enterprise by leveraging big data and plugload analytics to efficiently manage buildings.

In embodiments, IoT sensors and building automation control systems maybe configured to assist in optimizing floor space, identifying unusedequipment, automating efficient energy consumption, improving safety,and reducing environmental impact of buildings.

In embodiments, the platform 102 may manage total energy consumption ofsystems and equipment connected to the electrical network or to a set ofDERs 128. Some systems are almost always operational, while other piecesof equipment and machinery may be connected only occasionally. Bymaintaining an understanding of both the total daily electricalconsumption of a building and the role individual devices play in theoverall energy use of a specific system, the platform may forecast,provision, manage and control, optionally by AI or algorithm, the totalconsumption.

In embodiments, the platform 102 may track and leverage an understandingof o occupants' behavior. Activity levels, behavior patterns, andcomfort preferences of occupants may be a consideration for energyefficiency measures. This may include tracking various cyclical orseasonal factors. Over time, a building's energy generation, storageand/or consumption may follow predictable patterns that an IoT-basedanalytics platform can take into consideration when generating proposedsolutions.

In embodiments, the platform may enable or integrate with systems orplatforms for autonomous operations. For example, industrial sites, suchas oil rigs and power plants, require extensive monitoring forefficiency and safety because liquid, steam, or oil leakages can becatastrophic, costly, and wasteful. AI and machine learning may provideautonomous capabilities for power plants, such as those served by edgedevices, IoT devices, and onsite cameras and sensors. Models may bedeployed at the edge in power plants or on DERs 128, such as to usereal-time inferencing and pattern detection to identify faults, such asleaks, shaking, stress, or the like. Operators may use computer vision,deep learning, and intelligent video analytics (IVA) to monitor heavymachinery, detect potential hazards, and alert workers in real-time toprotect their health and safety, prevent accidents, and assign repairtechnicians for maintenance.

In embodiments, the platform may enable or integrate with systems orplatforms for pipeline optimization. For example, oil and gasenterprises may rely on finding the best-fit routes to transfer oil torefineries and eventually to fuel stations. Edge AI can calculate theoptimal flow of oil to ensure reliability of production and protectlong-term pipeline health. In embodiments, enterprises can inspectpipelines for defects that can lead to dangerous failures andautomatically alert pipeline operators.

Energy Provisioning and Governance Solutions

The energy provisioning and governance solutions 156 may includesolutions for governance of mining operations. Cobalt, nickel, and othermetals are fundamental components of the batteries that will be neededfor the green EV revolution. Amounts required to support the growingmarket will create economic pressure on mining operations, many of whichtake place in regions like the DRC where there is long history ofcorruption, child labor, and violence. Companies are exploring areaslike Greenland for cobalt, in part on the basis that it can offerreliable labor law enforcement, taxation compliance, and the like. Suchpromises can be made there and in other jurisdictions with greaterreliability through one or more mining governance solutions 542. Themining government solutions 542 may include mine-level IoT sensing ofthe mine environment, ground-penetrating sensing of unmined portions,mass spectrometry and computer vision-based sensing of mined materials,asset tagging of smart containers (e.g., detecting and recording openingand closing events to ensure that the material placed in a container isthe same material delivered at the end point), wearable devices fordetecting physiological status of miners, secure (e.g., blockchain- andDLT-based) recording and resolution of transactions andtransaction-related events, smart contracts for automatically allocatingproceeds (e.g., to tax authorities, to workers, and the like), and anautomated system for recording, reporting, and assessing compliance withcontractual, regulatory, and legal policy requirements. All of theabove, from base sensors to compliance reports can be optionallyrepresented in a digital twin that represents each mine owner oroperated by an enterprise.

The energy provisioning and governance solutions 156 may also include aset of carbon-aware energy solutions, where controls for operatingentities that generate (or capture) carbon are managed by datacollection through edge and IoT devices about current carbon generationor emission status and by automated generation of a set ofrecommendations and or control instructions to govern the operatingentities to satisfy policies, such as by keeping operations within arange that is offset by available carbon offset credits, or the like.

More detail on a variety of energy provisioning and governance solutions156 is provided below.

Localized Production Solutions

In embodiments, a set of localized production systems 158 may beintegrated with, linked to, or managed by the platform 102, such thatlocalized production demand can be met, particularly for goods that arevery costly to transport (e.g., food) or services where the cost ofenergy distribution has a large adverse impact on product or servicemargins (e.g., where there is a need for intensive computation in placeswhere the electrical grid is absent, lacks capacity, is unreliable, oris too expensive).

In embodiments, power management systems may converge with othersystems, such as building management systems, operational managementsystems, production systems, services systems, data centers, and othersto allow for enterprise-wide energy management.

FIG. 3: More Detail on Distributed Energy Generation Systems

Referring to FIG. 3 , a distributed energy generation systems 302 mayinclude wind turbines, solar photovoltaics (PV), flexible and/orfloating solar systems, fuel cells, modular nuclear reactors, nuclearbatteries, modular hydropower systems, microturbines and turbine arrays,reciprocating engines, combustion turbines, and cogeneration plants,among others. The distributed energy storage systems 304 may includebattery storage energy (including chemical batteries and others), moltensalt energy storage, electro-thermal energy storage (ETES),gravity-based storage, compressed fluid energy storage, pumpedhydroelectric energy storage (PHES), and liquid air energy storage(LAES), among others. The DER systems 128 may be managed by the platform102. In embodiments, the distributed energy storage systems 304 may beportable, such that units of energy may be transported to points of use,including points of use that are not connected to the conventional gridor ones where the conventional grid does not fully satisfy demand (e.g.,where greater peak power, more reliable continuous power, or othercapabilities are needed). Management may include the integration,coordination, and maximizing of return-on-investment (ROI) ondistributed energy resources (DERs), while providing reliability andflexibility for energy needs.

In embodiments, the DERs 128 may use various distributed energy deliverymethods and systems 308 having various energy delivery capabilities,including transmission lines (e.g., conventional grid and buildinginfrastructure), wireless energy transmission (including by coupled,resonant transfer between high-Q resonators, near-field energy transferand other methods), transportation of fluids, batteries, fuel cells,small nuclear systems, and the like), and others.

The mobile energy resources 124 include a wide range of resources forgeneration, storage, or delivery of energy at various scales;accordingly, the mobile energy resources 124 may comprise a subcategoryof the distributed energy resources 128 that have attributes ofmobility, such as where the mobile energy resources 124 are integratedinto a vehicle 310 (e.g., an electric vehicle, hybrid electric vehicle,hydrogen fuel cell vehicle, or the like, and in embodiments including aset of autonomous vehicles, which may be unmanned autonomous vehicles(UAVs), drones, or the like); where resources are integrated into orused by a mobile electronic device 312, or other mobile system; wherethe mobile energy resources 124 are portable resources 314 (includingwhere they are removable and replaceable from a vehicle or othersystem), and the like. As the mobile energy resources 124 and supportinginfrastructure (e.g., charging stations) scale in capacity andavailability, orchestration of the mobile energy resources 124 and otherDERs 128, optionally in coordination with available grid resources,takes on increased importance.

Resources involved in generation, storage, and transmission of energyare increasingly undergoing digital transformation. These digitizedresources 122 may include smart resources 318 (such as smart devices(e.g., thermostats), smart home devices (e.g., speakers), smartbuildings, smart wearable devices and many others that are enabled withprocessors, network connectivity, intelligent agents, and other onboardintelligence features) where intelligence features of the smartresources 318 can be used for energy orchestration, optimization,autonomy, control or the like and/or used to supply data for artificialintelligence and analytics in connection with the foregoing. Thedigitized resources 122 may also include IoT- and edge-digitizedresources 320, where sensors or other data collectors (such as datacollectors that monitor event logs, network packets, network trafficpatterns, networked device location patterns, or other available data)provide additional energy-related intelligence, such as in connectionwith energy generation, storage, transmission or consumption by legacyinfrastructure systems and devices ranging from large scale generatorsand transformers to consumer or business devices, appliances, and othersystems that are in proximity to a set of IoT or edge devices that canmonitor the same. Thus, IoT and edge device can provide digitalinformation about energy states and flows for such devices and systemswhether or not the devices and systems have onboard intelligencefeatures; for example, among many others, an IoT device can deploy acurrent sensor on a power line to an appliance to detect utilizationpatterns, or an edge networking device can detect whether another deviceor system connected to the device is in use (and in what state) bymonitoring network traffic from the other device. The digitizedresources 122 may also include cloud-aggregated resources 322 aboutenergy generation, storage, transmission, or use, such as by aggregatingdata across a fleet of similar resources that are owned or operated byan enterprise, that are used in connection with a defined workflow oractivity, or the like. The cloud-aggregated resources 322 may consumedata from the various data resources 110, from crowdsourcing, fromsensor data collection, from edge device data collection, and many othersources.

In embodiments, the digitized resources 122 may be used for a wide rangeof uses that involve or benefit from real time information about theattributes, states, or flows of energy generation, storage,transmission, or consumption, including to enable digital twins, such asadaptive energy digital twin systems 134 and/or stakeholder energydigital twins 148 and for various configured stakeholder energy edgesolutions 108.

Energy generation, storage, and consumption, particularly involvinggreen or renewable energy, have been the subject of intensive researchand development in recent decades, yielding higher peak power generationcapacity, increases in storage capacity, reductions in size and weight,improvements in intelligence and autonomy, and many others. The advancedenergy resources and systems 104 may include a wide range of advancedenergy infrastructure systems and devices that result from combinationsof features and capabilities. In embodiments, a set of flexible hybridenergy systems 324 may be provided that is adaptable to meet varyingenergy consumption requirements, such as ones that can provide more thanone kind of energy (e.g., solar or wind power) to meet baselinerequirements of an off-grid operation, along with a nuclear battery tosatisfy much higher peak power requirements, such as for temporary,resource intensive activities, such as operating a drill in a mine orrunning a large factory machine on a periodic basis. A wide variety ofsuch flexible, hybrid energy systems 324 are contemplated herein,including ones that are configured for modular interconnection withvarious types of localized production infrastructure as describedelsewhere herein. In embodiments, the advanced energy resources andsystems 104 may include advanced energy generation systems that drawpower from fluid flows, such as portable turbine arrays 328 that can betransported to points of consumption that are in proximity to wind orwater flows to substitute for or augment grid resources. The advancedenergy resources and systems 104 may also include modular nuclearsystems 330, including ones that are configured to use a nuclear batteryand ones that are configured with mechanical, electrical and datainterfaces to work with various consumption systems, including vehicles,localized production systems (as described elsewhere herein), smartbuildings, and many others. The nuclear systems 330 may include SMRs andother reactor types. The advanced energy resources and systems 104 mayinclude advanced storage systems 332, including advanced batteries andfuel cells, including batteries with onboard intelligence for autonomousmanagement, batteries with network connectivity for remote management,batteries with alternative chemistry (including green chemistry, such asnickel zinc), batteries made from alternative materials or structures(e.g., diamond batteries), batteries that incorporate generationcapacity (e.g., nuclear batteries), advanced fuel cells (e.g., cathodelayer fuels cells, alkaline fuel cells, polymer electrolyte fuel cells,solid oxide fuel cells, and many others).

FIG. 4: More Detail on Data Resources

Referring to FIG. 4 , the data resources for energy edge orchestration110 may include a wide range of public data sets, as well as private orproprietary data sets of an enterprise or individual. This may includedata sets generated by or passed through the edge and IoT networkingsystems 160, such as sensor data 402 (e.g., from sensors integrated intoor placed on machines or devices, sensors in wearable devices, andothers); network data 404 (such as data on network traffic volume,latency, congestion, quality of service (QoS), packet loss, error rate,and the like); event data 408 (such as data from event logs of edge andIoT devices, data from event logs of operating assets of an enterprise,event logs of wearable devices, event data detected by inspection oftraffic on application programming interfaces, event streams publishedby devices and systems, user interface interaction events (such ascaptured by tracking clicks, eye tracking and the like), user behavioralevents, transaction events (including financial transaction, databasetransactions and others), events within workflows (including directed,acyclic flows, iterative and/or looping flows, and the like), andothers); state data 410 (such as data indicating historical, current orpredicted/anticipated states of entities (such as machines, systems,devices, users, objects, individuals, and many others) and including awide range of attributes and parameters relevant to energy generation,storage, delivery or utilization of such entities); and/or combinationsof the foregoing (e.g., data indicating the state of an entity and of aworkflow involving the entity).

In embodiments, data resources may include, among many others,energy-relevant public data resources 162, such as energy grid data 422(such as historical, current and anticipated/predicted maintenancestatus, operating status, energy production status, capacity,efficiency, or other attribute of energy grid assets involved ingeneration, storage or transmission of energy); energy market data 424(such as historical, current and anticipated/predicted pricing data forenergy or energy-related entities, including spot market prices ofenergy based on location, type of consumption, type of generation andthe like, day-ahead or other futures market pricing for the same, costsof fuel, cost of raw materials involved (e.g., costs of materials usedin battery production), costs of energy-related activities, such asmineral extraction, and many others); location and mobility data 428(such as data indicating historical, current and/oranticipated/predicted locations or movements of groups of individuals(e.g., crowds attending large events, such as concerts, festivals,sporting events, conventions, and the like), data indicating historical,current and/or anticipated/predicted locations or movements of vehicles(such as used in transportation of people, goods, fuel, materials, andthe like), data indicating historical, current and/oranticipated/predicted locations or movements of points of productionand/or demand for resources, and others); and weather and climate data430 (such as indicating historical, current and/or anticipated/predictedenergy-relevant weather patterns, including temperature data,precipitation data, cloud cover data, humidity data, wind velocity data,wind direction data, storm data, barometric pressure data, and others).

In embodiments, the data resources for energy edge orchestration 110 mayinclude enterprise data resources 168, which may include, among manyothers, energy-relevant financial and transactional data 432 (such asindicating historical, current and/or anticipated/predicted state,event, or workflow data involving financial entities, assets, and thelike, such as data relating to prices and/or costs of energy and/or ofgoods and services, data related to transactions, data relating tovaluation of assets, balance sheet data, accounting data, data relatingto profits or losses, data relating to investments, interest rate data,data relating to debt and equity financing, capitalization data, andmany others); operational data 434 (such as indicating historical,current and/or anticipated/predicted states or flows of operatingentities, such as relating to operation of assets and systems used inproduction of goods and performance of services, relating to movement ofindividuals, devices, vehicles, machines and systems, relating tomaintenance and repair operations, and many others); human resourcesdata 438 (such as indicating historical, current and/oranticipated/predicted states, activities, locations or movements ofenterprise personnel); and sales and marketing data 440 (such asindicating historical, current and/or anticipated/predicted states oractivities of customers, advertising data, promotional data, loyaltyprogram data, customer behavioral data, demand planning data, pricingdata, and many others); and others.

In embodiments, the data resources for energy edge optimization 110 maybe handled by an adaptive energy data pipeline 164, which may leverageartificial intelligence capabilities of the platform 102 in order tooptimize the handling of the various data resources. Increases inprocessing power and storage capacity of devices are combining withwider deployment of edge and IoT devices to produce massive increases inthe scale and granularity of data of available data of the many typesdescribed herein. Accordingly, even more powerful networks like 5G, andanticipated 6G, are likely to have difficulty transmitting availablevolumes of data without problems of congestion, latency, errors, andreduced QoS. The adaptive energy edge data pipeline 164 can include aset of artificial intelligence capabilities for adapting the pipeline ofthe data resources 110 to enable more effective orchestration ofenergy-related activities, such as by optimizing various elements ofdata transmission in coordination with energy orchestration needs. Inembodiments, the adaptive energy data pipeline 164 may includeself-organizing data storage 412 (such as storing data on a device orsystem (e.g., an edge, IoT, or other networking device, cloud or datacenter system, on-premises system, or the like) based on the patterns orattributes of the data (e.g., patterns in volume of data over time, orother metrics), the content of the data, the context of the data (e.g.,whether the data relates high-stakes enterprise activities), and thelike). In embodiments, the adaptive energy data pipeline 164 may includeautomated, adaptive networking 414 (such as adaptive routing based onnetwork route conditions (including packet loss, error rates, QoS,congestion, cost/pricing and the like)), adaptive protocol selection(such as selecting among transport layer protocols (e.g., TCP or UDP)and others), adaptive routing based on RF conditions (e.g., adaptiveselection among available RF networks (e.g., Bluetooth, Zigbee, NFC, andothers)), adaptive filtering of data (e.g., DSP-based filtering of databased on recognition of whether a device is permitted to use RFcapability), adaptive slicing of network bandwidth, adaptive use ofcognitive and/or peer-to-peer network capacity, and others. Inembodiments, the adaptive energy data pipeline 164 may includeenterprise contextual adaptation 418, such as where data isautomatically processed based on context (such as operating context ofan enterprise (e.g., distinguishing between mission-critical and lesscritical operations, distinguishing between time-sensitive and otheroperations, distinguishing between context required for compliance withpolicy or law, and the like), transactional or financial context (e.g.,based on whether the data is required based on contractual requirements,based on whether the data is useful or necessary for real-timetransactional or financial benefits (e.g., time-sensitive arbitrageopportunities or damage-mitigation needs)), and many others). Inembodiments, the adaptive energy data pipeline 164 may includemarket-based adaptation 420, such as where storage, networking, or otheradaptation is based on historical, current and/or anticipated/predictedmarket factors (such as based on the cost of storage, transmissionand/or processing of the data (including the cost of energy used for thesame), the price, cost, and/or marginal profit of goods or services thatare produced based on the data, and many others).

In embodiments, the adaptive energy data pipeline 164 may adapt any andall aspects of data handling, including storage, routing, transmission,error correction, timing, security, extraction, transformation, loading,cleansing, normalization, filtering, compression, protocol selection(including physical layer, media access control layer and applicationlayer protocol selection), encoding, decoding, and others.

FIG. 5: More Detail on Configured Energy Edge Stakeholder SolutionsLocalized Production

Referring to FIG. 5 , the platform 102 may orchestrate the variousservices and capabilities described in order to configure the set ofconfigured stakeholder energy edge solutions 108, including the mobilitydemand solutions 152, enterprise optimization solutions 154, localizedproduction solutions 158, and energy provisioning and governancesolutions 108.

The set of localized production solutions 158 may include a set ofcomputation intensive solutions 522 where the demand for energy involvedin computation activities in a location is operationally significant,either in terms of overall energy usage or peak demand (particularlyones where location is a relevant factor in operations, but energyavailability may not be assured in adequate capacity, at acceptableprices), such as data center operations (e.g., to support high-frequencytrading operations that require low-latency and benefit from closeproximity to the computational systems of marketplaces and exchanges),operations using quantum computation, operations using very large neuralnetworks or computation-intensive artificial intelligence solutions(e.g., encoding and decoding systems used in cryptography), operationsinvolving complex optimization solutions (e.g., high-dimensionalitydatabase operations, analytics and the like, such as route optimizationin computer networks, behavioral targeting in marketing, routeoptimization in transportation), operations supporting cryptocurrencies(such as mining operations in cryptocurrencies that use proof-of-work orother computationally intensive approaches), operations where energy issourced from local energy sources (e.g., hydropower dams, wind farms,and the like), and many others.

The set of localized production solutions 158 may include a set oftransport cost mitigation solutions 524, such as ones where the cost ofenergy required to transport raw materials or finished goods to a pointof sale or to a point of use is a significant component in overall costof goods. The transport cost mitigation solutions 524 may configure aset of distributed energy resources 128 or other advanced energyresources 104 to provide energy that either supplements or substitutesfor conventional grid energy in order to allow localized production ofgoods that are conventionally produced remotely and transported bytransportation and logistics networks (e.g., long-haul trucking) topoints of sale or use. For example, crops that have high water contentcan be produced locally, such as in containers that are equipped withlighting systems, hydration systems, and the like in order to shift theenergy mix toward production of the crops, rather than transportation ofthe finished goods. The platform 102 may be used to optimize, at a fleetlevel, the mix of a set of localized, modular energy generation systemsor storage systems to support a set of localized production systems forheavy goods, such as by rotating the energy generation or storagesystems among the localized production systems to meet demand (e.g.,seasonal demand, demand based on crop cycles, demand based on marketcycles and the like).

The set of localized production solutions 158 may include a set ofremote production operation solutions 528, such as to orchestratedistributed energy resources 128 or other advanced energy resources 104to provide energy in a more optimal way to remote operations, such asmineral mining operations, energy exploration operations, drillingoperations, military operations, firefighting and other disasterresponse operations, forestry operations, and others where localizedenergy demand at given points of time periodically exceeds what can beprovided by the energy grid, or where the energy grid is not available.This may include orchestration of the routing and provisioning of afleet of portable energy storage systems (e.g., vehicles, batteries, andothers), the routing and provisioning of a fleet of portable renewableenergy generation systems (wind, solar, nuclear, hydropower and others),and the routing and provisioning of fuels (e.g., fuel cells).

The set of localized production solutions 158 may include a set offlexible and variable production solutions 530, such as where a set ofproduction assets (e.g., 3D printers, CNC machines, reactors,fabrication systems, conveyors and other components) are configured tointerface with a set of modular energy production systems, such as toaccept a combination of energy from the grid and from a localized energygeneration or storage source, and where the energy storage andgeneration systems are configured to be modular, removable, and portableamong the production assets in order to provide grid augmentation orsubstitution at a fleet level, without requiring a dedicated energyasset for each production asset. The platform 102 may be used toconfigure and orchestrate the set of energy assets and the set ofproduction assets in order to optimize localized production, includingbased on various factors noted herein, such as marketplace conditions inthe energy market and in the market for the goods and services of anenterprise.

Enterprise Optimization Solutions

The set of configured stakeholder energy edge solutions 108 may alsoinclude a set of enterprise optimization solutions 154, such as toprovide an enterprise with greater visibility into the role that energyplays in enterprise operations (such as to enable targeted, strategicinvestment in energy-relevant assets); greater agility in configuringoperations and transactions to meet operational and financial objectivesthat are driven at least in part by energy availability energy marketprices or the like; improved governance and control over energy-relatedfactors, such as carbon production, waste heat and pollution emissions;and improved efficiency in use of energy at any and all scales of use,ranging from electronic devices and smart buildings to factories andenergy extraction activities. The term “enterprise,” as used herein,may, except where context requires otherwise, include private and publicenterprises, including corporations, limited liability companies,partnerships, proprietorships and the like, non-governmentalorganizations, for-profit organizations, non-profit organizations,public-private partnerships, military organizations, first responderorganizations (police, fire departments, emergency medical services andthe like), private and public educational entities (schools, colleges,universities and others), governmental entities (municipal, county,state, provincial, regional, federal, national and international),agencies (local, state, federal, national and international, cooperative(e.g., treaty-based agencies), regulatory, environmental, energy,defense, civil rights, educational, and many others), and others.Examples provided in connection with a for-profit business should beunderstood to apply to other enterprises, and vice versa, except wherecontext precludes such applicability.

The enterprise optimization solutions 154 may include a set of smartbuilding solutions 512, where the platform 102 may be used toorchestrate energy generation, transmission, storage and/or consumptionacross a set of buildings owned or operated by the enterprise, such asby aggregating energy purchasing transactions across a fleet of smartbuildings, providing a set of shared mobile or portable energy unitsacross a fleet of smart buildings that are provisioned based oncontextual factors, such as utilization requirements, weather, marketprices and the like at each of the buildings, and many others.

Enterprise optimization solutions 154 may include a set of smart energydelivery solutions 514, where the platform 102 may be used toorchestrate delivery or energy at a favorable cost and at a favorabletime to a point of operational use. In embodiments, the platform 102may, for example, be used to time the routing of liquid fuel throughelements of a pipeline by automatically controlling switching points ofthe pipeline based on contextual factors, such as operationalutilization requirements, regulatory requirements, market prices, andthe like. In other embodiments, the platform 102 may be used toorchestrate routing of portable energy storage units or portable energygeneration units in order to deliver energy to augment or substitute forgrid energy capacity at a point and time of operational use. Inembodiments, the platform 102 may be used to orchestrate routing anddelivery of wireless power to deliver energy to a point and time of use.Energy delivery optimization may be based on market prices (historical,current, futures market, and/or predicted), based on operationalconditions (current and predicted), based on policies (e.g., dictatingpriority for certain uses) and many other factors.

Enterprise optimization solutions 154 may include a set of smart energytransaction solutions 518, where the platform 102 may be used toorchestrate transactions in energy or energy-related entities (e.g.,renewable energy credits (RECs), pollution abatement credits,carbon-reduction credits, or the like) across a fleet of enterpriseassets and/or operations, such as to optimize energy purchases and salesin coordination with energy-relevant operations at any and all scales ofenergy usage. This may include, in embodiments, aggregating and timingcurrent and futures market energy purchases across assets andoperations, automatically configuring purchases of shared generation,storage or delivery capacity for enterprise operational usage and thelike. The platform 102 may leverage blockchain, smart contract, andartificial intelligence capabilities, trained as described throughoutthis disclosure, to undertake such activities based on the operationalneeds, strategic objectives, and contextual factors of an enterprise, aswell as external contextual factors, such as market needs. For example,an anticipated need for energy by an enterprise machine may be providedas an event stream to a smart contract, which may automatically secure afuture energy delivery contract to meet the need, either by purchasinggrid-based energy from a provider or by ordering a portable energystorage unit, among other possibilities. The smart contract may beconfigured with intelligence, such as to time the purchase based on apredicted market price, which may be predicated, such as by anintelligent agent, based on historical market prices and currentcontextual factors.

Enterprise optimization solutions 154 may include a set of enterpriseenergy digital twin solutions 520, where the platform 102 may be used tocollect, monitor, store, process and represent in a digital twin a widerange of data representing states, conditions, operating parameters,events, workflows and other attributes of energy-relevant entities, suchas assets of the enterprise involved in operations, assets of externalentities that are relevant to the energy utilization or transactions ofthe enterprise (e.g., energy grid entities, pipelines, charginglocations, and the like), energy market entities (e.g., counterparties,smart contracts, blockchains, prices and the like). A user of the set ofenterprise energy digital twin solutions 520 may, for example, view aset of factories that are consuming energy and be presented with a viewthat indicates the relative efficiency of each factory, of individualmachines within the factory, or of components of the machines, such asto identify inefficient assets or components that should be replacedbecause the cost of replacement would be rapidly recouped by reducedenergy usage. The digital twin, in such example, may provide a visualindicator of inefficient assets, such as a red flag, may provide anordered list of the assets most benefiting from replacement, may providea recommendation that can be accepted by the user (e.g., triggering anorder for replacement), or the like. Digital twins may be role-based,adaptive based on context or market conditions, personalized, augmentedby artificial intelligence, and the like, in the many ways describedherein and in the documents incorporated by reference herein.

Mobility Demand Solutions

Referring still to FIG. 5 , the set of configured stakeholder energyedge solutions 108 may include a set of mobility demand solutions 152,such as where the platform 102 may be used to orchestrate energygeneration, storage, delivery and or consumption by or for a set ofmobile entities, such as a fleet of vehicles, a set of individuals, aset of mobile event production units, or a set of mobile factory units,among many others.

The set of mobility demand solutions 510 may include a set oftransportation solutions 502, such as where the platform 102 may be usedto orchestrate energy generation, storage, delivery and or consumptionby or for a set of vehicles, such as used to transport goods,passengers, or the like. The platform 102 may handle relevantoperational and contextual data, such as indicating needs, priorities,and the like for transportation, as well as relevant energy data, suchas the cost of energy used to transport entities using different modesof transportation at different points in time, and may provide a set ofrecommendations, or automated provisioning, of transportation in orderto optimize transportation operations while accounting fully for energycosts and prices. For example, among many others, an electric or hybridpassenger tour bus may be automatically routed to a scenic location thatis in proximity to a low cost, renewable energy charging station, sothat the bus can be recharged while the tourists experience thelocation, thus satisfying an energy-related objective (cost reduction)and an operational objective (customer satisfaction). An intelligentagent may be trained, using techniques described herein and in thedocuments incorporated by reference (such as by training robotic processautomation on a training set of expert interactions), to provide a setof recommendations for optimizing energy-related objectives and otheroperational objectives.

The set of mobility demand solutions 510 may include a set of mobileuser solutions 504, such as where the platform 102 may be used toorchestrate energy generation, storage, delivery and or consumption byor for a set of mobile users, such as users of mobile devices. Forexample, in anticipation of a large, temporary increase in the number ofpeople at a location (such as in a small city hosting a major sportingevent), the platform 102 may provide a set of recommendations for, orautomatically configure a set of orders for a set of portable rechargingunits to support charging of consumer devices.

The set of mobility demand solutions 510 may include a set of mobileevent production solutions 508, such as where the platform 102 may beused to orchestrate energy generation, storage, delivery and orconsumption by or for a set of mobile entities involved in production ofan event, such as a concert, sporting event, convention, circus, fair,revival, graduation ceremony, college reunion, festival, or the like.This may include automatically configuring a set of energy generation,storage or delivery units based on the operational configuration of theevent (e.g., to meet needs for lighting, food service, transportation,loudspeakers and other audio-visual elements, machines (e.g., 3Dprinters, video gaming machines, and the like), rides and others),automatically configuring such operational configuration based on energycapabilities, configuring one or more of energy or operational factorsbased on contextual factors (e.g., market prices, demographic factors ofattendees, or the like), and the like.

The set of mobility demand solutions 510 may include a set of mobilefactory solutions 510, such as where the platform 102 may be used toorchestrate energy generation, storage, delivery and or consumption byor for a set of mobile factory entities. These may includecontainer-based factories, such as where a 3D printer, CNC machine,closed-environment agriculture system, semiconductor fabricator, geneediting machine, biological or chemical reactor, furnace, or otherfactory machine is integrated into or otherwise contained in a shippingcontainer or other mobile factory housing, wherein the platform 102 may,based on a set of operational needs of the set of factory machines,configure a set of recommendations or instructions to provision energygeneration, storage, or delivery to meet the operational needs of theset of factory machine at a set of times and places. The configurationmay be based on energy factors, operational factors, and/or contextualfactors, such as market prices of goods and energy, needs of apopulation (such as disaster recovery needs), and many other factors.

Energy Provisioning and Governance Solutions

Referring still to FIG. 5 , the set of configured stakeholder energyedge solutions 108 may include a set of energy provisioning andgovernance solutions 156, such as where the platform 102 may be used toorchestrate energy generation, storage, delivery and or consumption byor for a set of entities based on a set of policies, regulations, laws,or the like, such as to facilitate compliance with company financialcontrol policies, government or company policies on carbon reduction,and many others.

The set of energy provisioning and governance solutions 156 may includea set of carbon-aware energy edge solutions 532, such as where a set ofpolicies regarding carbon generation may be explored, configured, andimplemented in the platform 102, such as to require energy production byone or more assets or operations to be monitored in order to trackcarbon generation or emissions, to require offsetting of such generationor emissions, or the like. In embodiments, energy generation controlinstructions (such as for a machine or set of machines) may beconfigured with embedded policy instructions, such as requiredconfirmation of available offsets before a machine is permitted togenerate energy (and carbon), or before a machine can exceed a givenamount of production in a given period. In embodiments, the embeddedpolicy instructions may include a set of override provisions that enablethe policy to be overridden (such as by a user, or based on contextualfactors, such as a declared state of emergency) for mission critical oremergency operations. Carbon generation, reduction and offsets may beoptimized across operations and assets of an enterprise, such as by anintelligent agent trained in various ways as described elsewhere in thisdisclosure.

The set of energy provisioning and governance solutions 156 may includea set of automated energy policy deployment solutions 534, such as wherea user may interact with a user interface to design, develop orconfigure (such as by entering rules or parameters) a set of policiesrelating to energy generation, storage, delivery and/or utilization,which may be handled by the platform, such as by presenting the policiesto users who interact with entities that are subject to the policies(such as interfaces of such entities and/or digital twins of suchentities, such as to provide alerts as to actions that risknoncompliance, to log noncompliant events, to recommend alternative,compliance options, and the like), by embedding the policies in controlsystems of entities that generate, store, deliver or use energy (suchthat operations of such entities are controlled in a manner that iscompliant with the policies), by embedding the policies in smartcontracts that enable energy-related transactions (such thattransactions are automatically executed in compliance with the policies,such that warnings or alerts are provided in the case of non-compliance,or the like), by setting policies that are automatically reconfiguredbased on contextual factors (such as operational and/or market factors)and others. In embodiments, an intelligent agent may be trained, such ason a training data set of historical data, on feedback from outcomes,and/or on a training data set of human policy-setting interactions, togenerate policies, to configure or modify policies, and/or to undertakeactions based on policies. A wide range of policies and configurationsmay be implemented, such as setting maximum energy usage for an entityfor a time period, setting maximum energy cost for an entity for a timeperiod, setting maximum carbon production for an entity for a timeperiod, setting maximum pollution emissions for an entity for a timeperiod, setting carbon offset requirements, setting renewable energycredit requirements, setting energy mix requirements (e.g., requiring aminimum fraction of renewable energy), setting profit margin minimumsbased on energy and other marginal costs for a production entity,setting minimum storage baselines for energy storage entities (such asto provide a margin of safety for disaster recovery), and many others.

The set of energy provisioning and governance solutions 156 may includea set of energy governance smart contract solutions 538, such as toallow a user of the platform 102 to design, generate, configure and/ordeploy a smart contract that automatically provides a degree ofgovernance of a set of energy transactions, such as where the smartcontract takes a set of operational, market or other contextual inputs(such as energy utilization information collected by edge devices aboutoperating assets) as inputs and automatically configures a set ofcontracts that are compliance with a set of policies for the purchase,sale, reservation, sharing, or other transaction for energy,energy-related credits, and the like. For example, a smart contract mayautomatically aggregate carbon offset credits needed to balance carbongeneration detected across a set of machines used in enterpriseoperations.

The set of energy provisioning and governance solutions 156 may includea set of automated energy financial control solutions 540, such as toallow a user of the platform 102 and/or an intelligent agent to design,generate, configure, or deploy a policy related to control of financialfactors related to energy generation, storage, delivery and/orutilization. For example, a user may set a policy requiring minimummarginal profit for a machine to continue operation, and the policy maybe presented to an operator of the machine, to a manager, or the like.As another example, the policy may be embedded in a control system forthe machine that takes a set of inputs needed to determine marginalprofitability (e.g., cost of inputs and other non-energy resources usedin production, cost of energy, predicted energy required to produceoutputs, and market price of outputs) and automatically determineswhether to continue production, and at what level, in order to maintainmarginal profitability. Such a policy may take further inputs, such asrelating to anticipated market and customer behavior, such as based onelasticity of demand for relevant outputs.

FIG. 6: More Detail on Intelligence Enablement Systems

Referring to FIG. 6 , further detail is provided as to embodiments ofthe intelligence enablement systems 112, including the intelligent datalayers 130, the distributed ledger and smart contract systems 132, theadaptive energy digital twin systems 134 and the energy simulationsystems 136.

The intelligent data layers 130 may undertake any of the wide range ofdata processing capabilities noted throughout this disclosure and thedocuments incorporated by reference herein, optionally autonomously,under user supervision, or with semi-supervision, including extraction,transformation, loading, normalization, cleansing, compression, routeselection, protocol selection, self-organization of storage, filtering,timing of transmission, encoding, decoding, and many others. Theintelligent data layers 130 may include energy generation data layers602 (such as producing and automatically configuring and routing streamsor batches of data relating to energy generation by a set of entities,such as operating assets of an enterprise), energy storage data layers604 (such as producing and automatically configuring and routing streamsor batches of data relating to energy storage by a set of entities, suchas operating assets of an enterprise or assets of a set of customers),energy delivery data layers 608 (such as producing and automaticallyconfiguring and routing streams or batches of data relating to energydelivery by a set of entities, such as delivery by transmission line, bypipeline, by portable energy storage, or others), and energy consumptiondata layers 610 (such as producing and automatically configuring androuting streams or batches of data relating to energy consumption by aset of entities, such as operating assets of an enterprise, a set ofcustomers, a set of vehicles, or the like).

The distributed ledger and smart contract systems 132 may provide a setof underlying capabilities to enable energy-related transactions, suchas purchases, sales, leases, futures contracts, and the like for energygeneration, storage, delivery, or consumption, as well as for relatedtypes of transactions, such as in renewable energy credits, carbonabatement credits, pollution abatement credits, leasing of assets,shared economy transactions for asset usage, shared consumptioncontracts, bulk purchases, provisioning of mobile resources, and manyothers. This may include a set of energy transaction blockchains 612 ordistributed ledgers to record energy transactions, including generation,storage, delivery, and consumption transactions. A set of energytransaction smart contracts 614 may operate on blockchain events andother input data to enable, configure, and execute the aforementionedtypes of transactions and others. In embodiments, a set of energytransaction intelligent agents 618 may be configured to design,generate, and deploy the smart contracts 614, to optimize transactionparameters, to automatically discover counterparties, arbitrageopportunities, and the like, to recommend and/or automatically initiatesteps to contract offers or execution, to resolve contracts uponcompletion based on blockchain data, and many other functions.

The adaptive energy digital twin systems 134 may include digital twinsof energy-related entities, such as operating assets of an enterprisethat generate, store, deliver, or consume energy, and may include mayinclude energy generation digital twins 622 (such as displaying contentfrom event logs, or from streams or batches of data relating to energygeneration by a set of entities, such as operating assets of anenterprise), energy storage digital twins 624 (such as displaying energystorage status information, usage patterns, or the like for a set ofentities, such as operating assets of an enterprise or assets of a setof customers), energy delivery digital twins 628 (such as displayingstatus data, events, workflows, and the like relating to energy deliveryby a set of entities, such as delivery by transmission line, bypipeline, by portable energy storage, or others), and energy consumptiondigital twins 630 (such as displaying data relating to energyconsumption by a set of entities, such as operating assets of anenterprise, a set of customers, a set of vehicles, or the like). Theadaptive energy digital twin systems 134 may include various types ofdigital twin described throughout this disclosure and/or the documentsincorporated herein by reference, such as ones fed by data streams fromedge and IoT devices, ones that adapt based on user role or context,ones that adapt based on market context, ones that adapt based onoperating context, and many others.

The set of energy simulation systems 136 may include a wide range ofsystems for the simulation of energy-related behavior based onhistorical patterns, current states (including contextual, operating,market and other information), and anticipated/predicted states ofentities involved in generation, storage, delivery and/or consumption ofenergy. This may include an energy generation simulation 632, energystorage simulation 634, energy delivery simulation 638 and energyconsumption simulation 640, among others. The simulation systems 136 mayemploy a wide range of simulation capabilities, such as 3D visualizationsimulation of behavior of physical, presentation of simulation outputsin a digital twin, generation of simulated financial outcomes for a setof different operational scenarios, generation of simulated operationaloutcomes, and many others. Simulation may be based on a set of models,such as models of the energy generation, storage, delivery and/orconsumption behavior of a machine or system, or a fleet of machines orsystems (which may be aggregated based on underlying models and/or basedon projection to a larger set from a subset of models). Models may beiteratively improved, such as by feedback of outcomes from operationsand/or by feedback comparing model-based predictions to actual outcomesand/or predictions by other models or human experts. Simulations may beundertaken using probabilistic techniques, by random walk or randomforest algorithms, by projections of trends from past data on currentconditions, or the like. Simulations may be based on behavioral models,such as models of enterprise or individual behavior based on variousfactors, including past behavior, economic factors (e.g., elasticity ofdemand or supply in response to price changes), energy utilizationmodels, and others. Simulations may use predictions from artificialintelligence, including artificial intelligence trained by machinelearning (including deep learning, supervised learning, semi-supervisedlearning, or the like). Simulations may be configured for presentationin augmented reality, virtual reality and/or mixed reality interfacesand systems (collectively referred to as “XR”), such as to enable a userto interact with aspects of a simulation in order to be trained tocontrol a machine, to set policies, to govern a factory or other entitythat includes multiple machines, to handle a fleet of machines orfactories, or the like. As one example among many, a simulation of afactory may simulate the energy consumption of all machines in thefactory while presenting other data, such as operational data, inputcosts, production costs, computation costs, market pricing data, andother content in the simulation. In the simulation, a user may configurethe factory, such as by setting output levels for each machine, and thesimulation may simulate profitability of the factory based on a varietyof simulated market conditions. Thus, the user may be trained toconfigure the factory under a variety of different market conditions.

FIG. 7: More Detail on AI-Based Energy Orchestration, Optimization, andAutomation Systems

Referring to FIG. 7 more detail is provided with respect to the set ofAI-based energy orchestration, optimization, and automation systems 114,each of which may use various other capabilities, services, functions,modules, components, or other elements of the platform 102 in order toorchestrate energy-related entities, workflows, or the like on behalf ofan enterprise or other user. Orchestration may, for example, use roboticprocess automation to facilitate automated orchestration ofenergy-related entities and resources based on training data sets and/orhuman supervision based on historical human interaction data. As anotherexample, orchestration may involve design, configuration, and deploymentof a set of intelligent agents, which may automatically orchestrate aset of energy-related workflows based on operational, market, contextualand other inputs. Orchestration may involve design, configuration, anddeployment of autonomous control systems, such as systems that controlenergy-related activities based on operational data collected by or fromonboard sensors, edge devices, IoT devices and the like. Orchestrationmay involve optimization, such as optimization of multivariate decisionsbased on simulation, optimization based on real-time inputs, and others.Orchestration may involve use of artificial intelligence for patternrecognition, forecasting and prediction, such as based on historicaldata sets and current conditions.

The set of AI-based energy orchestration, optimization, and automationsystems 114 may include the set of energy generation orchestrationsystems 138, the set of energy consumption orchestration systems 140,the set of energy storage orchestration systems 142, the set of energymarketplace orchestration systems 146 and the set of energy deliveryorchestration systems 147, among others.

The set of energy generation orchestration systems 138 may include a setof generation timing orchestration systems 702 and a set of locationorchestration systems 704, among others. The set of timing orchestrationsystems 702 may orchestrate the timing of energy generation, such as toensure that timing of generation meets mission critical or operationalneeds, complies with policies and plans, is optimized to improvefinancial or operational metrics and/or (in the case of energy generatedfor sale) is well-timed based on fluctuations of energy market prices.Generation timing orchestration can be based on models, simulations, ormachine learning on historical data sets. Generation timingorchestration can be based on current conditions (operating, market, andothers).

The set of generation location orchestration systems 704 may orchestratelocation of generation assets, including mobile or portable generationassets, such as portable generators, solar systems, wind systems,modular nuclear systems and others, as well as selection of locationsfor larger-scale, fixed infrastructure generation assets, such as powerplants, generators, turbines, and others, such as to ensure that for anygiven operational location, available generation capacity (baseline andpeak capacity) meets mission critical or operational needs, complieswith policies and plans, is optimized to improve financial oroperational metrics and/or (in the case of energy generated for sale) iswell-located based on local variations in energy market prices.Generation location orchestration can be based on models, simulations,or machine learning on historical data sets. Generation locationorchestration can be based on current conditions (operating, market, andothers).

The set of energy consumption orchestration systems 140 may include aset of consumption timing optimization systems 718 and a set ofoperational prioritization systems 720, among others. The set ofconsumption timing optimization systems 718 may orchestrate timingconsumption, such as to shift consumption for non-critical activities tolower-cost energy resources (e.g., by shifting to off-peak times toobtain lower electricity pricing for grid energy consumption, shiftingto lower cost resources (e.g., renewable energy systems in lieu of thegrid), to shift consumption to activities that are more profitable(e.g., to shift consumption to a machine that has a high marginal profitper time period based on current market and operating conditions (suchas detected by a combination of edge and IoT devices and market datasources), and the like).

The set of operational prioritization systems 720 may enable a user,intelligent agent, or the like to set operational priorities, such as byrule or policy, by setting target metrics (e.g., for efficiency,marginal profit production, or the like), by declaring mission-criticaloperations (e.g., for safety, disaster recovery and emergency systems),by declaring priority among a set of operating assets or activities, orthe like. In embodiments, energy consumption orchestration may takeinputs from operational prioritization to provide a set ofrecommendations or control instructions to optimize energy consumptionby a machine, components, a set of machines, a factory, or a fleet ofassets.

The set of energy storage orchestration systems 142 may include a set ofstorage location orchestration systems 708 and a set of margin-of-safetyorchestration systems 710. The set of storage location orchestrationsystems 708 may orchestrate location of storage assets, including mobileor portable generation assets, such as portable batteries, fuel cells,nuclear storage systems and others, as well as selection of locationsfor larger-scale, fixed infrastructure storage assets, such aslarge-scale arrays of batteries, fuel storage systems, thermal energystorage systems (e.g., using molten salt), gravity-based storagesystems, storage systems using fluid compression, and others, such as toensure that for any given operational location, available storagecapacity meets mission critical or operational needs, complies withpolicies and plans, is optimized to improve financial or operationalmetrics and/or (in the case of energy stored and provide for sale) iswell-located based on local variations in energy market prices. Storagelocation orchestration can be based on models, simulations, or machinelearning on historical data sets, such as behavioral models thatindicate usage patterns by individuals or enterprises. Storage locationorchestration can be based on current conditions (operating, market, andothers) and many other factors; for example, storage capacity can bebrought to locations where grid capacity is offline or unusuallyconstrained (e.g., for disaster recovery).

The set of margin of safety orchestration systems 710 may be used toorchestrate storage capacity to preserve a margin of safety, such as aminimum amount of stored energy to power mission critical systems (e.g.,life support systems, perimeter security systems, or the like) or highpriority systems (e.g., high-margin manufacturing) for a defined periodin case of loss of baseline energy capacity (e.g., due to an outage orbrownout of the grid) or inadequate renewable energy production (e.g.,when there is inadequate wind, water or solar power due to weatherconditions, drought, or the like). The minimum amount may be set by ruleor policy, or may be learned adaptively, such as by an intelligentagent, based on a training data set of outcomes and/or based onhistorical, current, and anticipated conditions (e.g., climate andweather forecasts). The margin of safety orchestration system 710 may,in embodiments, take inputs from the energy provisioning and governancesolutions 156.

The set of energy marketplace orchestration systems 146 may include aset of transaction aggregation systems 722 and a set of futures marketoptimization systems 724.

The set of transaction aggregation systems 722 systems may automaticallyorchestrate a set of energy-related transactions, such as purchases,sales, orders, futures contracts, hedging contracts, limit orders, stoploss orders, and others for energy generation, storage, delivery orconsumption, for renewable energy credits, for carbon abatement credits,for pollution abatement credits, or the like, such as to aggregate a setof smaller transactions into a bulk transaction, such as to takeadvantage of volume discounts, to ensure current or day-ahead pricingwhen favorable, to enable fractional ownership by a set of owners,operators, or consumers of a block of energy generation, storage, ordelivery capacity, or the like. For example, an enterprise may aggregateenergy purchases across a set of assets in different jurisdictions byuse of an intelligent agent that aggregates a set of futures marketenergy purchases across the jurisdiction and represents the aggregatedpurchases in a centralized location, such as an operating digital twinof the enterprise.

The set of futures market optimization systems 724 may automaticallyorchestrate aggregation of a set of futures markets contracts forenergy, renewable energy credits, for carbon offsets or abatementcredits, for pollution abatement credits, or the like based on aforecast of future energy needs for an individual or enterprise. Theforecast may be based on historical usage patterns, current operatingconditions, current market conditions, anticipated operational needs,and the like. The forecast may be generated using a predictive modeland/or by an intelligent agent, such as one based on machine learning onoutcomes, on human output, on human-labeled data, or the like. Theforecast may be generated by deep learning, supervised learning,semi-supervised learning, or the like. Based on the forecast, anintelligent agent may design, configure, and execute a series of futuresmarket transactions across various jurisdictions to meet anticipatedtiming, location, and type of needs.

The set of energy delivery orchestration systems 147 may include a setof delivery routing orchestration systems 712 and a set of energydelivery type orchestration systems 714.

The set of energy delivery routing orchestration systems 712 may usevarious components, modules, facilities, services, functions and otherelements of the platform 102 to orchestrate routing of energy delivery,such as based on location, timing and type of needs, availablegeneration and storage capacity at places of energy need, availableenergy sources for routing (e.g., liquid fuel, portable energygeneration systems, portable energy storage systems, and the like),available routes (e.g., main pipelines, pipeline branches, transmissionlines, wireless power transfer systems, and transportationinfrastructure (roads, railways and waterways, among others)), marketfactors (price of energy, price of goods, profit margins for productionactivities, timing of events that require energy, and others),environmental factors (e.g., weather), operational priorities, andothers. A set of artificial intelligence systems trained in various waysdisclosed herein may be trained to recommend or to configure a route,such as based on the foregoing inputs and a set of training data, suchas human routing activities, a route optimization model, iteration amonga large number of simulated scenarios, or the like, or combination ofany of the foregoing. For example, a set of control instructions maydirect valves and other elements of an energy pipeline to deliver anamount of fluid-based energy to a location while directing mobile orportable resources to another location that would otherwise have reducedenergy availability based on the pipeline routing instructions.

The set of energy delivery type orchestration systems 714 may usevarious components, modules, facilities, services, functions and otherelements of the platform 102 to orchestrate optimization of the type ofenergy delivery, such as based on location, timing and type of needs,available generation and storage capacity at places of energy need,available energy sources for routing (e.g., liquid fuel, portable energygeneration systems, portable energy storage systems, and the like),available routes (e.g., main pipelines, pipeline branches, transmissionlines, wireless power transfer systems, and transportationinfrastructure (roads, railways and waterways, among others)), marketfactors (price of energy, price of goods, profit margins for productionactivities, timing of events that require energy, and others),environmental factors (e.g., weather), operational priorities, andothers. A set of artificial intelligence systems trained in various waysdisclosed herein may be trained to recommend or to configure a mix ofenergy types, such as based on the foregoing inputs and a set oftraining data, such as human type selection activities, a delivery typeoptimization model, iteration among a large number of simulatedscenarios, or the like, or combination of any of the foregoing. Forexample, a set of recommendations or control instructions may select aset of portable, modular energy resources that are compatible with needs(e.g., specifying renewable sources where there is high storage capacityto meet operational needs, such that inexpensive, intermittent sourcesare preferred), while the instructions may select more expensive naturalgas energy where storage capacity is limited or absent and usage iscontinuous (such as for a 24/7 data center that operates remotely fromthe energy grid).

Many other examples of AI-based energy orchestration, optimization, andautomation 114 are provided throughout this disclosure.

FIG. 8: More Detail on Configurable Data and Intelligence Modules andServices

Referring to FIG. 8 the set of configurable data and intelligencemodules and services 118 may include the set of energy transactionenablement systems 144, the set of stakeholder energy digital twins 148and the set of data integrated microservices 150, among many others.These data and intelligence modules may include various components,modules, services, subsystems, and other elements needed to configure adata stream or batch, to configure intelligence to provide a particulartype of output, or the like, such as to enable other elements of theplatform 102 and/or various stakeholder solutions.

The set of energy transaction enablement systems 144 may include a setof counterparty and arbitrage discovery systems 802, a set of automatedtransaction configuration systems 804 and a set of energy investment anddivestiture recommendation systems 808, among others. The set ofcounterparty and arbitrage discovery systems 802 may be configured tooperate on various data sources related to operating energy needs,contextual factors, and a set of energy market, renewable energy credit,carbon offset, pollution abatement credit, or other energy-relatedmarket offers by a set of counterparties in order to determine arecommendation or selection of a set of counterparties and offers. Anintelligent agent of the counterparty and arbitrage discovery systems802 may initiate a transaction with a set of counterparties based on therecommendation or selection. Factors may include cost, counterpartyreliability, size of counterparty offer, timing, location of energyneeds, and many others.

The set of automated transaction configuration systems 804 mayautomatically or under human supervision recommend or automaticallyconfigure terms for a transaction, such as based on contextual factors(e.g., weather), historical, current, or anticipated/predicted marketdata (e.g., relating to energy pricing, costs of production, costs ofstorage, and the like), timing and location of operating needs, andother factors. Automation may be by artificial intelligence, such astrained on human configuration interactions, trained by deep learning onoutcomes, or trained by iterative improvement through a series of trialsand adjustments (e.g., of the inputs and/or weights of a neuralnetwork).

The set of energy investment and divestiture recommendation systems 808may automatically or under human supervision recommend or automaticallyconfigure terms for an investment or divestiture transaction, such asbased on contextual factors (e.g., weather), historical, current, oranticipated/predicted market data (e.g., relating to energy pricing,costs of production, costs of storage, and the like), timing andlocation of operating needs, and other factors. Automation may be byartificial intelligence, such as trained on human configurationinteractions, trained by deep learning on outcomes, or trained byiterative improvement through a series of trials and adjustments (e.g.,of the inputs and/or weights of a neural network). For example, the setof energy investment and divestiture recommendation systems 808 mayoutput a recommendation to invest in additional modular, portablegeneration units to support locations of planned energy explorationactivities or the divestiture of relatively inefficient factories, whereenergy costs are forecast to produce negative marginal profits.

The set of stakeholder energy digital twins 148 may include a set offinancial energy digital twins 810, a set of operational energy digitaltwins 812 and a set of executive energy digital twins 814, among manyothers. The set of financial energy digital twins 810 may, for example,represent a set of entities, such as operating assets of an enterprise,along with energy-related financial data, such as the cost of energybeing used or forecast to be used by a machine, component, factory, orfleet of assets, the price of energy that could be sold, the cost orprice of renewable energy credits available through use of renewableenergy generation capacity, the cost or price of carbon offsets neededto offset current of future anticipated operations, the cost ofpollution abatement offsets or credits, and the like. The financialenergy digital twins 810 may be integrated with other financialreporting systems and interfaces, such as enterprise resource planningsuites, financial accounting suites, tax systems, and others.

The set of operational energy digital twins 812 may, for example,represent operational entities involved in energy generation, storage,delivery, or consumption, along with relevant specification data,historical, current or anticipated/predicted operating states orparameters, and other information, such as to enable an operator to viewcomponents, machines, systems, factories, and various combinations andsets thereof, on an individual or aggregate level. The operationalenergy digital twins 812 may display energy data and energy-related datarelevant to operations, such as generation, storage, delivery andconsumption data, carbon production, pollution emissions, waste heatproduction, and the like. A set of intelligent agents may provide alertsin the digital twins. The digital twins may automatically adapt, such asby highlighting important changes, critical operations, maintenance, orreplacement needs, or the like. The operational energy digital twins 812may take data from onboard sensors, IoT devices, and edge devicespositioned at or near relevant operations, such as to provide real-time,current data.

The set of executive energy digital twins 814 may, for example, displayentities involved in energy generation, storage, delivery orconsumption, along with relevant specification data, historical, currentor anticipated/predicted operating states or parameters, and otherinformation, such as to enable an executive to view key performancemetrics driven by energy with respect to components, machines, systems,factories, and various combinations and sets thereof, on an individualor aggregate level. The executive energy digital twins 814 may displayenergy data and energy-related data relevant to executive decisions,such as generation, storage, delivery and consumption data, carbonproduction, pollution emissions, waste heat production, and the like, aswell as financial performance data, competitive market data, and thelike. A set of intelligent agents may provide alerts in the digitaltwins, such as configured to the role of the executive (e.g., financialdata to a CFO, risk management data to a chief legal officer, andaggregate performance data to a CEO or chief strategy officer. Theexecutive energy digital twins 814 may automatically adapt, such as byhighlighting important changes, critical operations, strategicopportunities, or the like. The executive energy digital twins 814 maytake data from onboard sensors, IoT devices, and edge devices positionedat or near relevant operations, such as to provide real-time, currentdata.

The set of data integrated microservices 150 may include a set of energymarket data services 818, a set of operational data services 820 and aset of other contextual data services 822, among many others.

The set of energy market data services 818 may provide a configured,filtered and/or otherwise processed feed of relevant market data, suchas market prices of the goods and services of an enterprise, a feed ofhistorical, current and/or futures market energy prices in the operatingjurisdictions of the enterprise (optionally weighted or ordered based onrelative energy usage across the jurisdictions), a feed of historicaland/or proposed transactions (optionally augmented with counterpartyinformation) configured according to a set of preferences of a user orenterprise (e.g., to show transactions relevant to the operatingrequirements or energy capacities of the enterprise), a feed ofhistorical, current or future renewable energy credit prices, a feed ofhistorical, current or future carbon offset prices, a feed ofhistorical, current or future pollution abatement credit prices, andothers.

The set of operational data services 820 may provide a configured,filtered and/or otherwise processed feed of operational data, such ashistorical, current, and anticipated/predicted states and events ofoperating assets of an enterprise, such as collected by sensors, IoTdevices and/or edge devices and or anticipated or inferred based on aset of models, analytic systems, and or operation of artificialintelligence systems, such as intelligent forecasting agents.

The set of other contextual data services 822 may provide a wide rangeof configured, filtered, or otherwise processed feeds of contextualdata, such as weather data, user behavior data, location data for apopulation, demographic data, psychographic data, and many others.

The configurable data integrated microservices of various types mayprovide various configured outputs, such as batches and files, databasereports, event logs, data streams, and others. Streams and feeds may beautomatically generated and pushed to other systems, services may bequeried and/or may be pulled from sources (e.g., distributed databases,data lakes, and the like), and may be pulled by application programminginterfaces.

Neural Network Examples

The foregoing neural networks may have a variety of nodes or neurons,which may perform a variety of functions on inputs, such as inputsreceived from sensors or other data sources, including other nodes.Functions may involve weights, features, feature vectors, and the like.Neurons may include perceptrons, neurons that mimic biological functions(such as of the human senses of touch, vision, taste, hearing, andsmell), and the like. Continuous neurons, such as with sigmoidalactivation, may be used in the context of various forms of neural net,such as where back propagation is involved.

In many embodiments, an expert system or neural network may be trained,such as by a human operator or supervisor, or based on a data set,model, or the like. Training may include presenting the neural networkwith one or more training data sets that represent values, such assensor data, event data, parameter data, and other types of data(including the many types described throughout this disclosure), as wellas one or more indicators of an outcome, such as an outcome of aprocess, an outcome of a calculation, an outcome of an event, an outcomeof an activity, or the like. Training may include training inoptimization, such as training a neural network to optimize one or moresystems based on one or more optimization approaches, such as Bayesianapproaches, parametric B ayes classifier approaches, k-nearest-neighborclassifier approaches, iterative approaches, interpolation approaches,Pareto optimization approaches, algorithmic approaches, and the like.Feedback may be provided in a process of variation and selection, suchas with a genetic algorithm that evolves one or more solutions based onfeedback through a series of rounds.

In embodiments, a plurality of neural networks may be deployed in acloud platform that receives data streams and other inputs collected(such as by mobile data collectors) in one or more energy edgeenvironments and transmitted to the cloud platform over one or morenetworks, including using network coding to provide efficienttransmission. In the cloud platform, optionally using massively parallelcomputational capability, a plurality of different neural networks ofvarious types (including modular forms, structure-adaptive forms,hybrids, and the like) may be used to undertake prediction,classification, control functions, and provide other outputs asdescribed in connection with expert systems disclosed throughout thisdisclosure. The different neural networks may be structured to competewith each other (optionally including use evolutionary algorithms,genetic algorithms, or the like), such that an appropriate type ofneural network, with appropriate input sets, weights, node types andfunctions, and the like, may be selected, such as by an expert system,for a specific task involved in a given context, workflow, environmentprocess, system, or the like.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a feed forwardneural network, which moves information in one direction, such as from adata input, like a data source related to at least one resource orparameter related to a transactional environment, such as any of thedata sources mentioned throughout this disclosure, through a series ofneurons or nodes, to an output. Data may move from the input nodes tothe output nodes, optionally passing through one or more hidden nodes,without loops. In embodiments, feed forward neural networks may beconstructed with various types of units, such as binary McCulloch-Pittsneurons, the simplest of which is a perceptron.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a capsule neuralnetwork, such as for prediction, classification, or control functionswith respect to a transactional environment, such as relating to one ormore of the machines and automated systems described throughout thisdisclosure.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a radial basisfunction (RBF) neural network, which may be preferred in some situationsinvolving interpolation in a multi-dimensional space (such as whereinterpolation is helpful in optimizing a multi-dimensional function,such as for optimizing a data marketplace as described here, optimizingthe efficiency or output of a power generation system, a factory system,or the like, or other situation involving multiple dimensions. Inembodiments, each neuron in the RBF neural network stores an examplefrom a training set as a “prototype.” Linearity involved in thefunctioning of this neural network offers RBF the advantage of nottypically suffering from problems with local minima or maxima.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a radial basisfunction (RBF) neural network, such as one that employs a distancecriterion with respect to a center (e.g., a Gaussian function). A radialbasis function may be applied as a replacement for a hidden layer, suchas a sigmoidal hidden layer transfer, in a multi-layer perceptron. AnRBF network may have two layers, such as where an input is mapped ontoeach RBF in a hidden layer. In embodiments, an output layer may comprisea linear combination of hidden layer values representing, for example, amean predicted output. The output layer value may provide an output thatis the same as or similar to that of a regression model in statistics.In classification problems, the output layer may be a sigmoid functionof a linear combination of hidden layer values, representing a posteriorprobability. Performance in both cases is often improved by shrinkagetechniques, such as ridge regression in classical statistics. Thiscorresponds to a prior belief in small parameter values (and thereforesmooth output functions) in a Bayesian framework. RBF networks may avoidlocal minima, because the only parameters that are adjusted in thelearning process are the linear mapping from hidden layer to outputlayer. Linearity ensures that the error surface is quadratic andtherefore has a single minimum. In regression problems, this can befound in one matrix operation. In classification problems, the fixednon-linearity introduced by the sigmoid output function may be handledusing an iteratively re-weighted least squares function or the like.

RBF networks may use kernel methods such as support vector machines(SVM) and Gaussian processes (where the RBF is the kernel function). Anon-linear kernel function may be used to project the input data into aspace where the learning problem can be solved using a linear model.

In embodiments, an RBF neural network may include an input layer, ahidden layer and a summation layer. In the input layer, one neuronappears in the input layer for each predictor variable. In the case ofcategorical variables, N−1 neurons are used, where N is the number ofcategories. The input neurons may, in embodiments, standardize the valueranges by subtracting the median and dividing by the interquartilerange. The input neurons may then feed the values to each of the neuronsin the hidden layer. In the hidden layer, a variable number of neuronsmay be used (determined by the training process). Each neuron mayconsist of a radial basis function that is centered on a point with asmany dimensions as a number of predictor variables. The spread (e.g.,radius) of the RBF function may be different for each dimension. Thecenters and spreads may be determined by training. When presented with avector of input values from the input layer, a hidden neuron may computea Euclidean distance of the test case from the neuron's center point andthen apply the RBF kernel function to this distance, such as using thespread values. The resulting value may then be passed to the summationlayer. In the summation layer, the value coming out of a neuron in thehidden layer may be multiplied by a weight associated with the neuronand may add to the weighted values of other neurons. This sum becomesthe output. For classification problems, one output is produced (with aseparate set of weights and summation units) for each target category.The value output for a category is the probability that the case beingevaluated has that category. In training of an RBF, various parametersmay be determined, such as the number of neurons in a hidden layer, thecoordinates of the center of each hidden-layer function, the spread ofeach function in each dimension, and the weights applied to outputs asthey pass to the summation layer. Training may be used by clusteringalgorithms (such as k-means clustering), by evolutionary approaches, andthe like.

In embodiments, a recurrent neural network may have a time-varying,real-valued (more than just zero or one) activation (output). Eachconnection may have a modifiable real-valued weight. Some of the nodesare called labeled nodes, some output nodes, and others hidden nodes.For supervised learning in discrete time settings, training sequences ofreal-valued input vectors may become sequences of activations of theinput nodes, one input vector at a time. At each time step, eachnon-input unit may compute its current activation as a nonlinearfunction of the weighted sum of the activations of all units from whichit receives connections. The system can explicitly activate (independentof incoming signals) some output units at certain time steps.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a self-organizingneural network, such as a Kohonen self-organizing neural network, suchas for visualization of views of data, such as low-dimensional views ofhigh-dimensional data. The self-organizing neural network may applycompetitive learning to a set of input data, such as from one or moresensors or other data inputs from or associated with a transactionalenvironment, including any machine or component that relates to thetransactional environment. In embodiments, the self-organizing neuralnetwork may be used to identify structures in data, such as unlabeleddata, such as in data sensed from a range of data sources about orsensors in or about in a transactional environment, where sources of thedata are unknown (such as where events may be coming from any of a rangeof unknown sources). The self-organizing neural network may organizestructures or patterns in the data, such that they can be recognized,analyzed, and labeled, such as identifying market behavior structures ascorresponding to other events and signals.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a recurrent neuralnetwork, which may allow for a bi directional flow of data, such aswhere connected units (e.g., neurons or nodes) form a directed cycle.Such a network may be used to model or exhibit dynamic temporalbehavior, such as involved in dynamic systems, such as a wide variety ofthe automation systems, machines and devices described throughout thisdisclosure, such as an automated agent interacting with a marketplacefor purposes of collecting data, testing spot market transactions,execution transactions, and the like, where dynamic system behaviorinvolves complex interactions that a user may desire to understand,predict, control and/or optimize. For example, the recurrent neuralnetwork may be used to anticipate the state of a market, such as oneinvolving a dynamic process or action, such as a change in state of aresource that is traded in or that enables a marketplace oftransactional environment. In embodiments, the recurrent neural networkmay use internal memory to process a sequence of inputs, such as fromother nodes and/or from sensors and other data inputs from or about thetransactional environment, of the various types described herein. Inembodiments, the recurrent neural network may also be used for patternrecognition, such as for recognizing a machine, component, agent, orother item based on a behavioral signature, a profile, a set of featurevectors (such as in an audio file or image), or the like. In anon-limiting example, a recurrent neural network may recognize a shiftin an operational mode of a marketplace or machine by learning toclassify the shift from a training data set consisting of a stream ofdata from one or more data sources of sensors applied to or about one ormore resources.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a modular neuralnetwork, which may comprise a series of independent neural networks(such as ones of various types described herein) that are moderated byan intermediary. Each of the independent neural networks in the modularneural network may work with separate inputs, accomplishing sub tasksthat make up the task the modular network as whole is intended toperform. For example, a modular neural network may comprise a recurrentneural network for pattern recognition, such as to recognize what typeof machine or system is being sensed by one or more sensors that areprovided as input channels to the modular network and an RBF neuralnetwork for optimizing the behavior of the machine or system onceunderstood. The intermediary may accept inputs of each of the individualneural networks, process them, and create output for the modular neuralnetwork, such an appropriate control parameter, a prediction of state,or the like.

Combinations among any of the pairs, triplets, or larger combinations,of the various neural network types described herein, are encompassed bythe present disclosure. This may include combinations where an expertsystem uses one neural network for recognizing a pattern (e.g., apattern indicating a problem or fault condition) and a different neuralnetwork for self-organizing an activity or workflow based on therecognized pattern (such as providing an output governing autonomouscontrol of a system in response to the recognized condition or pattern).This may also include combinations where an expert system uses oneneural network for classifying an item (e.g., identifying a machine, acomponent, or an operational mode) and a different neural network forpredicting a state of the item (e.g., a fault state, an operationalstate, an anticipated state, a maintenance state, or the like). Modularneural networks may also include situations where an expert system usesone neural network for determining a state or context (such as a stateof a machine, a process, a work flow, a marketplace, a storage system, anetwork, a data collector, or the like) and a different neural networkfor self-organizing a process involving the state or context (e.g., adata storage process, a network coding process, a network selectionprocess, a data marketplace process, a power generation process, amanufacturing process, a refining process, a digging process, a boringprocess, or other process described herein).

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a physical neuralnetwork where one or more hardware elements is used to perform orsimulate neural behavior. In embodiments, one or more hardware neuronsmay be configured to stream voltage values, current values, or the likethat represent sensor data, such as to calculate information from analogsensor inputs representing energy consumption, energy production, or thelike, such as by one or more machines providing energy or consumingenergy for one or more transactions. One or more hardware nodes may beconfigured to stream output data resulting from the activity of theneural net. Hardware nodes, which may comprise one or more chips,microprocessors, integrated circuits, programmable logic controllers,application-specific integrated circuits, field-programmable gatearrays, or the like, may be provided to optimize the machine that isproducing or consuming energy, or to optimize another parameter of somepart of a neural net of any of the types described herein. Hardwarenodes may include hardware for acceleration of calculations (such asdedicated processors for performing basic or more sophisticatedcalculations on input data to provide outputs, dedicated processors forfiltering or compressing data, dedicated processors for de-compressingdata, dedicated processors for compression of specific file or datatypes (e.g., for handling image data, video streams, acoustic signals,thermal images, heat maps, or the like), and the like. A physical neuralnetwork may be embodied in a data collector, including one that may bereconfigured by switching or routing inputs in varying configurations,such as to provide different neural net configurations within the datacollector for handling different types of inputs (with the switching andconfiguration optionally under control of an expert system, which mayinclude a software-based neural net located on the data collector orremotely). A physical, or at least partially physical, neural networkmay include physical hardware nodes located in a storage system, such asfor storing data within a machine, a data storage system, a distributedledger, a mobile device, a server, a cloud resource, or in atransactional environment, such as for accelerating input/outputfunctions to one or more storage elements that supply data to or takedata from the neural net. A physical, or at least partially physical,neural network may include physical hardware nodes located in a network,such as for transmitting data within, to or from an energy edgeenvironment, such as for accelerating input/output functions to one ormore network nodes in the net, accelerating relay functions, or thelike. In embodiments of a physical neural network, an electricallyadjustable resistance material may be used for emulating the function ofa neural synapse. In embodiments, the physical hardware emulates theneurons, and software emulates the neural network between the neurons.In embodiments, neural networks complement conventional algorithmiccomputers. They are versatile and can be trained to perform appropriatefunctions without the need for any instructions, such as classificationfunctions, optimization functions, pattern recognition functions,control functions, selection functions, evolution functions, and others.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a multilayeredfeed forward neural network, such as for complex pattern classificationof one or more items, phenomena, modes, states, or the like. Inembodiments, a multilayered feed forward neural network may be trainedby an optimization technical, such as a genetic algorithm, such as toexplore a large and complex space of options to find an optimum, ornear-optimum, global solution. For example, one or more geneticalgorithms may be used to train a multilayered feed forward neuralnetwork to classify complex phenomena, such as to recognize complexoperational modes of machines, such as modes involving complexinteractions among machines (including interference effects, resonanceeffects, and the like), modes involving non-linear phenomena, modesinvolving critical faults, such as where multiple, simultaneous faultsoccur, making root cause analysis difficult, and others. In embodiments,a multilayered feed forward neural network may be used to classifyresults from monitoring of a marketplace, such as monitoring systems,such as automated agents, that operate within the marketplace, as wellas monitoring resources that enable the marketplace, such as computing,networking, energy, data storage, energy storage, and other resources.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a feed-forward,back-propagation multi-layer perceptron (MLP) neural network, such asfor handling one or more remote sensing applications, such as for takinginputs from sensors distributed throughout various transactionalenvironments. In embodiments, the MLP neural network may be used forclassification of transactional environments and resource environments,such as lending markets, spot markets, forward markets, energy markets,renewable energy credit (REC) markets, networking markets, advertisingmarkets, spectrum markets, ticketing markets, rewards markets, computemarkets, and others mentioned throughout this disclosure, as well asphysical resources and environments that produce them, such as energyresources (including renewable energy environments, mining environments,exploration environments, drilling environments, and the like, includingclassification of geological structures (including underground featuresand above ground features), classification of materials (includingfluids, minerals, metals, and the like), and other problems. This mayinclude fuzzy classification.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use astructure-adaptive neural network, where the structure of a neuralnetwork is adapted, such as based on a rule, a sensed condition, acontextual parameter, or the like. For example, if a neural network doesnot converge on a solution, such as classifying an item or arriving at aprediction, when acting on a set of inputs after some amount oftraining, the neural network may be modified, such as from a feedforward neural network to a recurrent neural network, such as byswitching data paths between some subset of nodes from unidirectional tobi directional data paths. The structure adaptation may occur undercontrol of an expert system, such as to trigger adaptation uponoccurrence of a trigger, rule or event, such as recognizing occurrenceof a threshold (such as an absence of a convergence to a solution withina given amount of time) or recognizing a phenomenon as requiringdifferent or additional structure (such as recognizing that a system isvarying dynamically or in a non-linear fashion). In one non-limitingexample, an expert system may switch from a simple neural networkstructure like a feed forward neural network to a more complex neuralnetwork structure like a recurrent neural network, a convolutionalneural network, or the like upon receiving an indication that acontinuously variable transmission is being used to drive a generator,turbine, or the like in a system being analyzed.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an autoencoder,autoassociator or Diabolo neural network, which may be similar to amultilayer perceptron (MLP) neural network, such as where there may bean input layer, an output layer and one or more hidden layers connectingthem. However, the output layer in the auto-encoder may have the samenumber of units as the input layer, where the purpose of the MLP neuralnetwork is to reconstruct its own inputs (rather than just emitting atarget value). Therefore, the auto encoders may operate as anunsupervised learning model. An auto encoder may be used, for example,for unsupervised learning of efficient codings, such as fordimensionality reduction, for learning generative models of data, andthe like. In embodiments, an auto-encoding neural network may be used toself-learn an efficient network coding for transmission of analog sensordata from a machine over one or more networks or of digital data fromone or more data sources. In embodiments, an auto-encoding neuralnetwork may be used to self-learn an efficient storage approach forstorage of streams of data.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a probabilisticneural network (PNN), which in embodiments may comprise a multi-layer(e.g., four-layer) feed forward neural network, where layers may includeinput layers, hidden layers, pattern/summation layers and an outputlayer. In an embodiment of a PNN algorithm, a parent probabilitydistribution function (PDF) of each class may be approximated, such asby a Parzen window and/or a non-parametric function. Then, using the PDFof each class, the class probability of a new input is estimated, andBayes' rule may be employed, such as to allocate it to the class withthe highest posterior probability. A PNN may embody a Bayesian networkand may use a statistical algorithm or analytic technique, such asKernel Fisher discriminant analysis technique. The PNN may be used forclassification and pattern recognition in any of a wide range ofembodiments disclosed herein. In one non-limiting example, aprobabilistic neural network may be used to predict a fault condition ofan engine based on collection of data inputs from sensors andinstruments for the engine.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a time delayneural network (TDNN), which may comprise a feed forward architecturefor sequential data that recognizes features independent of sequenceposition. In embodiments, to account for time shifts in data, delays areadded to one or more inputs, or between one or more nodes, so thatmultiple data points (from distinct points in time) are analyzedtogether. A time delay neural network may form part of a larger patternrecognition system, such as using a perceptron network. In embodiments,a TDNN may be trained with supervised learning, such as where connectionweights are trained with back propagation or under feedback. Inembodiments, a TDNN may be used to process sensor data from distinctstreams, such as a stream of velocity data, a stream of accelerationdata, a stream of temperature data, a stream of pressure data, and thelike, where time delays are used to align the data streams in time, suchas to help understand patterns that involve understanding of the variousstreams (e.g., changes in price patterns in spot or forward markets).

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a convolutionalneural network (referred to in some cases as a CNN, a ConvNet, a shiftinvariant neural network, or a space invariant neural network), whereinthe units are connected in a pattern similar to the visual cortex of thehuman brain. Neurons may respond to stimuli in a restricted region ofspace, referred to as a receptive field. Receptive fields may partiallyoverlap, such that they collectively cover the entire (e.g., visual)field. Node responses can be calculated mathematically, such as by aconvolution operation, such as using multilayer perceptrons that useminimal preprocessing. A convolutional neural network may be used forrecognition within images and video streams, such as for recognizing atype of machine in a large environment using a camera system disposed ona mobile data collector, such as on a drone or mobile robot. Inembodiments, a convolutional neural network may be used to provide arecommendation based on data inputs, including sensor inputs and othercontextual information, such as recommending a route for a mobile datacollector. In embodiments, a convolutional neural network may be usedfor processing inputs, such as for natural language processing ofinstructions provided by one or more parties involved in a workflow inan environment. In embodiments, a convolutional neural network may bedeployed with a large number of neurons (e.g., 100,000, 500,000 ormore), with multiple (e.g., 4, 5, 6 or more) layers, and with many(e.g., millions) of parameters. A convolutional neural net may use oneor more convolutional nets.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a regulatoryfeedback network, such as for recognizing emergent phenomena (such asnew types of behavior not previously understood in a transactionalenvironment).

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a self-organizingmap (SOM), involving unsupervised learning. A set of neurons may learnto map points in an input space to coordinates in an output space. Theinput space can have different dimensions and topology from the outputspace, and the SOM may preserve these while mapping phenomena intogroups.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a learning vectorquantization neural net (LVQ). Prototypical representatives of theclasses may parameterize, together with an appropriate distance measure,in a distance-based classification scheme.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an echo statenetwork (ESN), which may comprise a recurrent neural network with asparsely connected, random hidden layer. The weights of output neuronsmay be changed (e.g., the weights may be trained based on feedback). Inembodiments, an ESN may be used to handle time series patterns, such as,in an example, recognizing a pattern of events associated with a market,such as the pattern of price changes in response to stimuli.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a Bi-directional,recurrent neural network (BRNN), such as using a finite sequence ofvalues (e.g., voltage values from a sensor) to predict or label eachelement of the sequence based on both the past and the future context ofthe element. This may be done by adding the outputs of two RNNs, such asone processing the sequence from left to right, the other one from rightto left. The combined outputs are the predictions of target signals,such as ones provided by a teacher or supervisor. A bi-directional RNNmay be combined with a long short-term memory RNN.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a hierarchical RNNthat connects elements in various ways to decompose hierarchicalbehavior, such as into useful subprograms. In embodiments, ahierarchical RNN may be used to manage one or more hierarchicaltemplates for data collection in a transactional environment.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a stochasticneural network, which may introduce random variations into the network.Such random variations can be viewed as a form of statistical sampling,such as Monte Carlo sampling.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a genetic scalerecurrent neural network. In such embodiments, a RNN (often a LSTM) isused where a series is decomposed into a number of scales where everyscale informs the primary length between two consecutive points. A firstorder scale consists of a normal RNN, a second order consists of allpoints separated by two indices and so on. The Nth order RNN connectsthe first and last node. The outputs from all the various scales may betreated as a committee of members, and the associated scores may be usedgenetically for the next iteration.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a committee ofmachines (CoM), comprising a collection of different neural networksthat together “vote” on a given example. Because neural networks maysuffer from local minima, starting with the same architecture andtraining, but using randomly different initial weights often givesdifferent results. A CoM tends to stabilize the result.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an associativeneural network (ASNN), such as involving an extension of committee ofmachines that combines multiple feed forward neural networks and ak-nearest neighbor technique. It may use the correlation betweenensemble responses as a measure of distance amid the analyzed cases forthe kNN. This corrects the bias of the neural network ensemble. Anassociative neural network may have a memory that can coincide with atraining set. If new data become available, the network instantlyimproves its predictive ability and provides data approximation(self-learns) without retraining. Another important feature of ASNN isthe possibility to interpret neural network results by analysis ofcorrelations between data cases in the space of models.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an instantaneouslytrained neural network (ITNN), where the weights of the hidden and theoutput layers are mapped directly from training vector data.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a spiking neuralnetwork, which may explicitly consider the timing of inputs. The networkinput and output may be represented as a series of spikes (such as adelta function or more complex shapes). SNNs can process information inthe time domain (e.g., signals that vary over time, such as signalsinvolving dynamic behavior of markets or transactional environments).They are often implemented as recurrent networks.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a dynamic neuralnetwork that addresses nonlinear multivariate behavior and includeslearning of time-dependent behavior, such as transient phenomena anddelay effects. Transients may include behavior of shifting marketvariables, such as prices, available quantities, availablecounterparties, and the like.

In embodiments, cascade correlation may be used as an architecture andsupervised learning algorithm, supplementing adjustment of the weightsin a network of fixed topology. Cascade-correlation may begin with aminimal network, then automatically trains and add new hidden units oneby one, creating a multi-layer structure. Once a new hidden unit hasbeen added to the network, its input-side weights may be frozen. Thisunit then becomes a permanent feature-detector in the network, availablefor producing outputs or for creating other, more complex featuredetectors. The cascade-correlation architecture may learn quickly,determine its own size and topology, and retain the structures it hasbuilt even if the training set changes and requires no back-propagation.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a neuro-fuzzynetwork, such as involving a fuzzy inference system in the body of anartificial neural network. Depending on the type, several layers maysimulate the processes involved in a fuzzy inference, such asfuzzification, inference, aggregation and defuzzification. Embedding afuzzy system in a general structure of a neural net as the benefit ofusing available training methods to find the parameters of a fuzzysystem.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a compositionalpattern-producing network (CPPN), such as a variation of an associativeneural network (ANN) that differs the set of activation functions andhow they are applied. While typical ANNs often contain only sigmoidfunctions (and

sometimes Gaussian functions), CPPNs can include both types of functionsand many others. Furthermore, CPPNs may be applied across the entirespace of possible inputs, so that they can represent a complete image.Since they are compositions of functions, CPPNs in effect encode imagesat infinite resolution and can be sampled for a particular display atwhatever resolution is optimal.

This type of network can add new patterns without re-training. Inembodiments, methods and systems described herein that involve an expertsystem or self-organization capability may use a one-shot associativememory network, such as by creating a specific memory structure, whichassigns each new pattern to an orthogonal plane using adjacentlyconnected hierarchical arrays.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a hierarchicaltemporal memory (HTM) neural network, such as involving the structuraland algorithmic properties of the neocortex. HTM may use a biomimeticmodel based on memory-prediction theory. HTM may be used to discover andinfer the high-level causes of observed input patterns and sequences.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a holographicassociative memory (HAM) neural network, which may comprise an analog,correlation-based, associative, stimulus-response system. Informationmay be mapped onto the phase orientation of complex numbers. The memoryis effective for associative memory tasks, generalization and patternrecognition with changeable attention.

In embodiments, various embodiments involving network coding may be usedto code transmission data among network nodes in neural net, such aswhere nodes are located in one or more data collectors or machines in atransactional environment.

Referring to FIG. 9 through FIG. 37 , embodiments of the presentdisclosure, including ones involving expert systems, self-organization,machine learning, artificial intelligence, and the like, may benefitfrom the use of a neural net, such as a neural net trained for patternrecognition, for classification of one or more parameters,characteristics, or phenomena, for support of autonomous control, andother purposes. References to a neural net throughout this disclosureshould be understood to encompass a wide range of different types ofneural networks, machine learning systems, artificial intelligencesystems, and the like, such as dual-process artificial neural networks(DPANN), feed forward neural networks, radial basis function neuralnetworks, self-organizing neural networks (e.g., Kohonen self-organizingneural networks), recurrent neural networks, modular neural networks,artificial neural networks, physical neural networks, multi-layeredneural networks, convolutional neural networks, hybrids of neuralnetworks with other expert systems (e.g., hybrid fuzzy logic—neuralnetwork systems), Autoencoder neural networks, probabilistic neuralnetworks, time delay neural networks, convolutional neural networks,regulatory feedback neural networks, radial basis function neuralnetworks, recurrent neural networks, Hopfield neural networks, Boltzmannmachine neural networks, self-organizing map (SOM) neural networks,learning vector quantization (LVQ) neural networks, fully recurrentneural networks, simple recurrent neural networks, echo state neuralnetworks, long short-term memory neural networks, bi-directional neuralnetworks, hierarchical neural networks, stochastic neural networks,genetic scale RNN neural networks, committee of machines neuralnetworks, associative neural networks, physical neural networks,instantaneously trained neural networks, spiking neural networks,neocognitron neural networks, dynamic neural networks, cascading neuralnetworks, neuro-fuzzy neural networks, compositional pattern-producingneural networks, memory neural networks, hierarchical temporal memoryneural networks, deep feed forward neural networks, gated recurrent unit(GCU) neural networks, auto encoder neural networks, variational autoencoder neural networks, de-noising auto encoder neural networks, sparseauto-encoder neural networks, Markov chain neural networks, restrictedBoltzmann machine neural networks, deep belief neural networks, deepconvolutional neural networks, de-convolutional neural networks, deepconvolutional inverse graphics neural networks, generative adversarialneural networks, liquid state machine neural networks, extreme learningmachine neural networks, echo state neural networks, deep residualneural networks, support vector machine neural networks, neural Turingmachine neural networks, and/or holographic associative memory neuralnetworks, or hybrids or combinations of the foregoing, or combinationswith other expert systems, such as rule-based systems, model-basedsystems (including ones based on physical models, statistical models,flow-based models, biological models, biomimetic models, and the like).

In embodiments, the platform 102 includes a dual process artificialneural network (DPANN) system. The DPANN system includes an artificialneural network (ANN) having behaviors and operational processes (such asdecision-making) that are products of a training system and a retrainingsystem. The training system is configured to perform automatic, trainedexecution of ANN operations. The retraining system performs effortful,analytical, intentional retraining of the ANN, such as based on one ormore relevant aspects of the ANN, such as memory, one or more input datasets (including time information with respect to elements in such datasets), one or more goals or objectives (including ones that may varydynamically, such as periodically and/or based on contextual changes,such as ones relating to the usage context of the ANN), and/or others.In cases involving memory-based retraining, the memory may includeoriginal/historical training data and refined training data. The DPANNsystem includes a dual process learning function (DPLF) 902 configuredto manage and perform an ongoing data retention process. The DPLF 902(including, where applicable, memory management process) facilitateretraining and refining of behavior of the ANN. The DPLF 902 provides aframework by which the ANN creates outputs such as predictions,classifications, recommendations, conclusions and/or other outputs basedon a historic inputs, new inputs, and new outputs (including outputsconfigured for specific use cases, including ones determined byparameters of the context of utilization (which may include performanceparameters such as latency parameters, accuracy parameters, consistencyparameters, bandwidth utilization parameters, processing capacityutilization parameters, prioritization parameters, energy utilizationparameters, and many others).

In embodiments, the DPANN system stores training data, thereby allowingfor constant retraining based on results of decisions, predictions,and/or other operations of the ANN, as well as allowing for analysis oftraining data upon the outputs of the ANN. The management of entitiesstored in the memory allows the construction and execution of newmodels, such as ones that may be processed, executed or otherwiseperformed by or under management of the training system. The DPANNsystem uses instances of the memory to validate actions (e.g., in amanner similar to the thinking of a biological neural network (includingretrospective or self-reflective thinking about whether actions thatwere undertaken under a given situation where optimal) and performtraining of the ANN, including training that intentionally feeds the ANNwith appropriate sets of memories (i.e., ones that produce favorableoutcomes given the performance requirements for the ANN).

In embodiments, FIG. 9 illustrates an exemplary process of the DPLF 902.The DPLF 902 may be or include the continued process retention of one ormore training datasets and/or memories stored in the memory over time.The DPLF 902 thereby allows the ANN to apply existing neural functionsand draw upon sets of past events (including ones that are intentionallyvaried and/or curated for distinct purposes), such as to frameunderstanding of and behavior within present, recent, and/or newscenarios, including in simulations, during training processes, and infully operational deployments of the ANN. The DPLF 902 may provide theANN with a framework by which the ANN may analyze, evaluate, and/ormanage data, such as data related to the past, present and future. Assuch, the DPLF 902 plays a crucial role in training and retraining theANN via the training system and the retraining system.

In embodiments, the DPLF 902 is configured to perform a dual-processoperation to manage existing training processes and is also configuredto manage and/or perform new training processes, i.e., retrainingprocesses. In embodiments, each instance of the ANN is trained via thetraining system and configured to be retrained via the retrainingsystem. The ANN encodes training and/or retraining datasets, stores thedatasets, and retrieves the datasets during both training via thetraining system and retraining via the retraining system. The DPANNsystem may recognize whether a dataset (the term dataset in this contextoptionally including various subsets, supersets, combinations,permutations, elements, metadata, augmentations, or the like, relativeto a base dataset used for training or retraining), storage activity,processing operation and/or output, has characteristics that nativelyfavor the training system versus the retraining system based on itsrespective inputs, processing (e.g., based on its structure, type,models, operations, execution environment, resource utilization, or thelike) and/or outcomes (including outcome types, performance requirements(including contextual or dynamic requirements), and the like. Forexample, the DPANN system may determine that poor performance of thetraining system on a classification task may indicate a novel problemfor which the training of the ANN was not adequate (e.g., in type ofdata set, nature of input models and/or feedback, quantity of trainingdata, quality of tagging or labeling, quality of supervision, or thelike), for which the processing operations of the ANN are notwell-suited (e.g., where they are prone to known vulnerabilities due tothe type of neural network used, the type of models used, etc.), andthat may be solved by engaging the retraining system to retrain themodel to teach the model to learn to solve the new classificationproblem (e.g., by feeding it many more labeled instances of correctlyclassified items). With periodic or continuous evaluation of theperformance of the ANN, the DPANN system may subsequently determine thathighly stable performance of the ANN (such as where only smallimprovements of the ANN occur over many iterations of retraining by theretraining system) indicates readiness for the training system toreplace the retraining system (or be weighted more favorably where bothare involved). Over longer periods of time, cycles of varyingperformance may emerge, such as where a series of novel problems emerge,such that the retraining system of the DPANN is serially engaged, asneeded, to retrain the ANN and/or to augment the ANN by providing asecond source of outputs (which may be fused or combined with ANNoutputs to provide a single result (with various weightings acrossthem), or may be provided in parallel, such as enabling comparison,selection, averaging, or context- or situation-specific application ofthe respective outputs).

In embodiments, the ANN is configured to learn new functions inconjunction with the collection of data according to the dual-processtraining of the ANN via the training system and the retraining system.The DPANN system performs analysis of the ANN via the training systemand performs initial training of the ANN such that the ANN gains newinternal functions (or internal functions are subtracted or modified,such as where existing functions are not contributing to favorableoutcomes). After the initial training, the DPANN system performsretraining of the ANN via the retraining system. To perform theretraining, the retraining system evaluates the memory and historicprocessing of the ANN to construct targeted DPLF 902 processes forretraining. The DPLF 902 processes may be specific to identifiedscenarios. The ANN processes can run in parallel with the DPLF 902processes. By way of example, the ANN may function to operate aparticular make and model of a self-driving car after the initialtraining by the training system. The DPANN system may perform retrainingof the functions of the ANN via the retraining system, such as to allowthe ANN to operate a different make and model of car (such as one withdifferent cameras, accelerometers and other sensors, different physicalcharacteristics, different performance requirements, and the like), oreven a different kind of vehicle, such as a bicycle or a spaceship.

In embodiments, as quality of outputs and/or operations of the ANNimproves, and as long as the performance requirements and the context ofutilization for the ANN remain fairly stable, performing thedual-process training process can become a decreasingly demandingprocess. As such, the DPANN system may determine that fewer neurons ofthe ANN are required to perform operations and/or processes of the ANN,that performance monitoring can be less intensive (such as with longerintervals between performance checks), and/or that the retraining is nolonger necessary (at least for a period of time, such as until along-term maintenance period arrives and/or until there are significantshifts in context of utilization). As the ANN continues to improve uponexisting functions and/or add new functions via the dual-processtraining process, the ANN may perform other, at times more“intellectually-demanding” (e.g., retraining intensive) taskssimultaneously. For example, utilizing dual process-learned knowledge ofa function or process being trained, the ANN can solve an unrelatedcomplex problem or make a retraining decision simultaneously. Theretraining may include supervision, such as where an agent (e.g., humansupervisor or intelligent agent) directs the ANN to a retrainingobjective (e.g., “master this new function”) and provides a set oftraining tasks and feedback functions (such as supervisory grading) forthe retraining. In-embodiments, the ANN can be used to organize thesupervision, training and retraining of other dual process-trained ANNs,to seed such training or retraining, or the like.

In embodiments, one or more behaviors and operational processes (such asdecision-making) of the ANN may be products of training and retrainingprocesses facilitated by the training system and the retraining system,respectively. The training system may be configured to perform automatictraining of ANN, such as by continuously adding additional instances oftraining data as it is collected by or from various data sources. Theretraining system may be configured to perform effortful, analytical,intentional retraining of the ANN, such as based on memory (e.g., storedtraining data or refined training data) and/or optionally based onreasoning or other factors. For example, in a deployment managementcontext, the training system may be associated with a standard responseby the ANN, while the retraining system may implement DPLF 902retraining and/or network adaptation of the ANN. In some cases,retraining of the ANN beyond the factory, or “out-of-the-box,” traininglevel may involve more than retraining by the retraining system.Successful adjustment of the ANN by one or more network adaptations maybe dependent on the operation of one or more network adjustments of thetraining system.

In embodiments, the training system may facilitate fast operating by andtraining of the ANN by applying existing neural functions of the ANNbased on training of the ANN with previous datasets. Standardoperational activities of the ANN that may draw heavily on the trainingsystem may include one or more of the methods, processes, workflows,systems, or the like described throughout this disclosure and thedocuments incorporated herein, such as, without limitation: definedfunctions within networking (such as discovering available networks andconnections, establishing connections in networks, provisioning networkbandwidth among devices and systems, routing data within networks,steering traffic to available network paths, load balancing acrossnetworking resources, and many others); recognition and classification(such as of images, text, symbols, objects, video content, music andother audio content, speech content, and many others); spoken words;prediction of states and events (such as prediction of failure modes ofmachines or systems, prediction of events within workflows, predictionsof behavior in shopping and other activities, and many others); control(such as controlling autonomous or semi-autonomous systems, automatedagents (such as automated call-center operations, chat bots, and thelike) and others); and/or optimization and recommendation (such as forproducts, content, decisions, and many others). ANNs may also besuitable for training datasets for scenarios that only require output.The standard operational activities may not require the ANN to activelyanalyze what is being asked of the ANN beyond operating on well-defineddata inputs, to calculate well-defined outputs for well-defined usecases. The operations of the training system and/or the retrainingsystem may be based on one or more historic data training datasets andmay use the parameters of the historic data training datasets tocalculate results based on new input values and may be performed withsmall or no alterations to the ANN or its input types. In embodiments,an instance of the training system can be trained to classify whetherthe ANN is capable of performing well in a given situation, such as byrecognizing whether an image or sound being classified by the ANN is ofa type that has historically been classified with a high accuracy (e.g.,above a threshold).

In embodiments, network adaptation of the ANN by one or both of thetraining system and the retraining system may include a number ofdefined network functions, knowledge, and intuition-like behavior of theANN when subjected to new input values. In such embodiments, theretraining system may apply the new input values to the DPLF 902 systemto adjust the functional response of the ANN, thereby performingretraining of the ANN. The DPANN system may determine that retrainingthe ANN via network adjustment is necessary when, for example, withoutlimitation, functional neural networks are assigned activities andassignments that require the ANN to provide a solution to a novelproblem, engage in network adaptation or other higher-order cognitiveactivity, apply a concept outside of the domain in which the DPANN wasoriginally designed, support a different context of deployment (such aswhere the use case, performance requirements, available resources, orother factors have changed), or the like. The ANN can be trained torecognize where the retraining system is needed, such as by training theANN to recognize poor performance of the training system, highvariability of input data sets relative to the historical data sets usedto train the training system, novel functional or performancerequirements, dynamic changes in the use case or context, or otherfactors. The ANN may apply reasoning to assess performance and providefeedback to the retraining system. The ANN may be trained and/orretrained to perform intuitive functions, optionally including by acombinatorial or re-combinatorial process (e.g., including geneticprogramming wherein inputs (e.g., data sources), processes/functions(e.g., neural network types and structures), feedback, and outputs, orelements thereof, are arranged in various permutations and combinationsand the ANN is tested in association with each (whether in simulationsor live deployments), such as in a series of rounds, or evolutionarysteps, to promote favorable variants until a preferred ANN, or preferredset of ANNs is identified for a given scenario, use case, or set ofrequirements). This may include generating a set of input “ideas” (e.g.,combinations of different conclusions about cause-and-effect in adiagnostic process) for processing by the retraining system andsubsequent training and/or by an explicit reasoning process, such as aBayesian reasoning process, a casuistic or conditional reasoningprocess, a deductive reasoning process, an inductive reasoning process,or others (including combinations of the above) as described in thisdisclosure or the documents incorporated herein by reference.

In embodiments, the DPLF 902 may perform an encoding process of the DPLF902 to process datasets into a stored form for future use, such asretraining of the ANN by the retraining system. The encoding processenables datasets to be taken in, understood, and altered by the DPLF 902to better support storage in and usage from the memory. The DPLF 902 mayapply current functional knowledge and/or reasoning to consolidate newinput values. The memory can include short-term memory (STM) 906,long-term memory (LTM) 912, or a combination thereof. The datasets maybe stored in one or both of the STM 906 and the LTM 912. The STM 906 maybe implemented by the application of specialized behaviors inside theANN (such as recurrent neural network, which may be gated or un-gated,or long-term short-term neural networks). The LTM 912 may be implementedby storing scenarios, associated data, and/or unprocessed data that canbe applied to the discovery of new scenarios. The encoding process mayinclude processing and/or storing, for example, visual encoding data(e.g., processed through a Convolution Neural Network), acoustic sensorencoding data (e.g., how something sounds, speech encoding data (e.g.,processed through a deep neural network (DNN), optionally including forphoneme recognition), semantic encoding data of words, such to determinesemantic meaning, e.g., by using a Hidden Markov Model (HMM); and/ormovement and/or tactile encoding data (such as operation onvibration/accelerometer sensor data, touch sensor data, positional orgeolocation data, and the like). While datasets may enter the DPLF 902system through one of these modes, the form in which the datasets arestored may differ from an original form of the datasets and maypass-through neural processing engines to be encoded into compressedand/or context-relevant format. For example, an unsupervised instance ofthe ANN can be used to learn the historic data into a compressed format.

In embodiments, the encoded datasets are retained within the DPLF 902system. Encoded datasets are first stored in short-term DPLF 902, i.e.,STM 906. For example, sensor datasets may be primarily stored in STM906, and may be kept in STM 906 through constant repetition. Thedatasets stored in the STM 906 are active and function as a kind ofimmediate response to new input values. The DPANN system may removedatasets from STM 906 in response to changes in data streams due to, forexample, running out of space in STM 906 as new data is imported,processed and/or stored. For example, it is viable for short-term DPLF902 to only last between 15 and 30 seconds. STM 906 may only store smallamounts of data typically embedded inside the ANN.

In embodiments, the DPANN system may measure attention based onutilization of the training system, of the DPANN system as a whole,and/or the like, such as by consuming various indicators of attention toand/or utilization of outputs from the ANN and transmitting suchindicators to the ANN in response (similar to a “moment of recognition”in the brain where attention passes over something and the cognitivesystem says “aha!”). In embodiments, attention can be measured by thesheer amount of the activity of one or both of the systems on the datastream. In embodiments, a system using output from the ANN canexplicitly indicate attention, such as by an operator directing the ANNto pay attention to a particular activity (e.g., to respond to adiagnosed problem, among many other possibilities). The DPANN system maymanage data inputs to facilitate measures of attention, such as byprompting and/or calculating greater attention to data that has highinherent variability from historical patterns (e.g., in rates of change,departure from norm, etc.), data indicative of high variability inhistorical performance (such as data having similar characteristics todata sets involved in situations where the ANN performed poorly intraining), or the like.

In embodiments, the DPANN system may retain encoded datasets within theDPLF 902 system according to and/or as part of one or more storageprocesses. The DPLF 902 system may store the encoded datasets in LTM 912as necessary after the encoded datasets have been stored in STM 906 anddetermined to be no longer necessary and/or low priority for a currentoperation of the ANN, training process, retraining process, etc. The LTM912 may be implemented by storing scenarios, and the DPANN system mayapply associated data and/or unprocessed data to the discovery of newscenarios. For example, data from certain processed data streams, suchas semantically encoded datasets, may be primarily stored in LTM 912.The LTM 912 may also store image (and sensor) datasets in encoded form,among many other examples.

In embodiments, the LTM 912 may have relatively high storage capacity,and datasets stored within LTM 912 may, in some scenarios, beeffectively stored indefinitely. The DPANN system may be configured toremove datasets from the LTM 912, such as by passing LTM 912 datathrough a series of memory structures that have increasingly longretrieval periods or increasingly high threshold requirements to triggerutilization (similar to where a biological brain “thinks very hard” tofind precedent to deal with a challenging problem), thereby providingincreased salience of more recent or more frequently used memories whileretaining the ability to retrieve (with more time/effort) older memorieswhen the situation justifies more comprehensive memory utilization. Assuch, the DPANN system may arrange datasets stored in the LTM 912 on atimeline, such as by storing the older memories (measured by time oforigination and/or latest time of utilization) on a separate and/orslower system, by penalizing older memories by imposing artificialdelays in retrieval thereof, and/or by imposing threshold requirementsbefore utilization (such as indicators of high demand for improvedresults). Additionally or alternatively, LTM 912 may be clusteredaccording to other categorization protocols, such as by topic. Forexample, all memories proximal in time to a periodically recognizedperson may be clustered for retrieval together, and/or all memories thatwere related to a scenario may be clustered for retrieval together.

In embodiments, the DPANN system may modularize and link LTM 912datasets, such as in a catalog, a hierarchy, a cluster, a knowledgegraph (directed/acyclic or having conditional logic), or the like, suchas to facilitate search for relevant memories. For example, all memorymodules that have instances involving a person, a topic, an item, aprocess, a linkage of n-tuples of such things (e.g., all memory modulesthat involve a selected pair of entities), etc. The DPANN system mayselect sub-graphs of the knowledge graph for the DPLF 902 to implementin one or more domain-specific and/or task-specific uses, such astraining a model to predict robotic or human agent behavior by usingmemories that relate to a particular set of robotic or human agents,and/or similar robotic or human agents. The DPLF 902 system may cachefrequently used modules for different speed and/or probability ofutilization. High value modules (e.g., ones with high-quality outcomes,performance characteristics, or the like) can be used for otherfunctions, such as selection/training of STM 906 keep/forget processes.

In embodiments, the DPANN system may modularize and link LTM datasets,such as in various ways noted above, to facilitate search for relevantmemories. For example, memory modules that have instances involving aperson, a topic, an item, a process, a linkage of n-tuples of suchthings (such as all memory modules that involve a selected pair ofentities), or all memories associated with a scenario, etc., may belinked and searched. The DPANN system may select subsets of the scenario(e.g., sub-graphs of a knowledge graph) for the DPLF 902 for adomain-specific and/or task-specific use, such as training a model topredict robotic or human agent behavior by using memories that relate toa particular set of robotic or human agents and/or similar robotic orhuman agents. Frequently used modules or scenarios can be cached fordifferent speed/probability of utilization, or other performancecharacteristics. High value modules or scenarios (ones wherehigh-quality outcomes results) can be used for other functions, such asselection/training of STM 906 keep/forget processes, among others.

In embodiments, the DPANN system may perform LTM planning, such as tofind a procedural course of action for a declaratively described systemto reach its goals while optimizing overall performance measures. TheDPANN system may perform LTM planning when, for example, a problem canbe described in a declarative way, the DPANN system has domain knowledgethat should not be ignored, there is a structure to a problem that makesthe problem difficult for pure learning techniques, and/or the ANN needsto be trained and/or retrained to be able to explain a particular courseof action taken by the DPANN system. In embodiments, the DPANN systemmay be applied to a plan recognition problem, i.e., the inverse of aplanning problem: instead of a goal state, one is given a set ofpossible goals, and the objective in plan recognition is to find outwhich goal was being achieved and how.

In embodiments, the DPANN system may facilitate LTM scenario planning byusers to develop long-term plans. For example, LTM scenario planning forrisk management use cases may place added emphasis on identifyingextreme or unusual, yet possible, risks and opportunities that are notusually considered in daily operations, such as ones that are outside abell curve or normal distribution, but that in fact occur withgreater-than-anticipated frequency in “long tail” or “fat tail”situations, such as involving information or market pricing processes,among many others. LTM scenario planning may involve analyzingrelationships between forces (such as social, technical, economic,environmental, and/or political trends) in order to explain the currentsituation, and/or may include providing scenarios for potential futurestates.

In embodiments, the DPANN system may facilitate LTM scenario planningfor predicting and anticipating possible alternative futures along withthe ability to respond to the predicted states. The LTM planning may beinduced from expert domain knowledge or projected from currentscenarios, because many scenarios (such as ones involving results ofcombinatorial processes that result in new entities or behaviors) havenever yet occurred and thus cannot be projected by probabilistic meansthat rely entirely on historical distributions. The DPANN system mayprepare the application to LTM 912 to generate many different scenarios,exploring a variety of possible futures to the DPLM for both expectedand surprising futures. This may be facilitated or augmented by geneticprogramming and reasoning techniques as noted above, among others.

In embodiments, the DPANN system may implement LTM scenario planning tofacilitate transforming risk management into a plan recognition problemand apply the DPLF 902 to generate potential solutions. LTM scenarioinduction addresses several challenges inherent to forecast planning.LTM scenario induction may be applicable when, for example, models thatare used for forecasting have inconsistent, missing, unreliableobservations; when it is possible to generate not just one but manyfuture plans; and/or when LTM domain knowledge can be captured andencoded to improve forecasting (e.g., where domain experts tend tooutperform available computational models). LTM scenarios can be focusedon applying LTM scenario planning for risk management. LTM scenariosplanning may provide situational awareness of relevant risk drivers bydetecting emerging storylines. In addition, LTM scenario planning cangenerate future scenarios that allow DPLM, or operators, to reasonabout, and plan for, contingencies and opportunities in the future.

In embodiments, the DPANN system may be configured to perform aretrieval process via the DPLF 902 to access stored datasets of the ANN.The retrieval process may determine how well the ANN performs withregard to assignments designed to test recall. For example, the ANN maybe trained to perform a controlled vehicle parking operation, wherebythe autonomous vehicle returns to a designated spot, or the exit, byassociating a prior visit via retrieval of data stored in the LTM 912.The datasets stored in the STM 906 and the LTM may be retrieved bydiffering processes. The datasets stored in the STM 906 may be retrievedin response to specific input and/or by order in which the datasets arestored, e.g., by a sequential list of numbers. The datasets stored inthe LTM 912 may be retrieved through association and/or matching ofevents to historic activities, e.g., through complex associations andindexing of large datasets.

In embodiments, the DPANN system may implement scenario monitoring as atleast a part of the retrieval process. A scenario may provide contextfor contextual decision-making processes. In embodiments, scenarios mayinvolve explicit reasoning (such as cause-and-effect reasoning,Bayesian, casuistic, conditional logic, or the like, or combinationsthereof) the output of which declares what LTM-stored data is retrieved(e.g., a timeline of events being evaluated and other timelinesinvolving events that potentially follow a similar cause-and-effectpattern). For example, diagnosis of a failure of a machine or workflowmay retrieve historical sensor data as well as LTM data on variousfailure modes of that type of machine or workflow (and/or a similarprocess involving a diagnosis of a problem state or condition,recognition of an event or behavior, a failure mode (e.g., a financialfailure, contract breach, or the like), or many others).

In embodiments, FIG. 10 through FIG. 37 depict exemplary neural networksand FIG. 10 depicts a legend showing the various components of theneural networks depicted throughout FIG. 10 to FIG. 37 . FIG. 10 depictsvarious neural net components depicted in cells that are assignedfunctions and requirements. In embodiments, the various neural netexamples may include (from top to bottom in the example of FIG. 10 ):back fed data/sensor input cells, data/sensor input cells, noisy inputcells, and hidden cells. The neural net components also includeprobabilistic hidden cells, spiking hidden cells, output cells, matchinput/output cells, recurrent cells, memory cells, different memorycells, kernels, and convolution or pool cells.

In embodiments, FIG. 11 depicts an exemplary perceptron neural networkthat may connect to, integrate with, or interface with the platform 102.The platform may also be associated with further neural net systems suchas a feed forward neural network (FIG. 12 ), a radial basis neuralnetwork (FIG. 13 ), a deep feed forward neural network (FIG. 14 ), arecurrent neural network (FIG. 15 ), a long/short term neural network(FIG. 16 ), and a gated recurrent neural network (FIG. 17 ). Theplatform may also be associated with further neural net systems such asan auto encoder neural network (FIG. 18 ), a variational neural network(FIG. 19 ), a denoising neural network (FIG. 20 ), a sparse neuralnetwork (FIG. 21 ), a Markov chain neural network (FIG. 22 ), and aHopfield network neural network (FIG. 23 ). The platform may further beassociated with additional neural net systems such as a Boltzmannmachine neural network (FIG. 24 ), a restricted BM neural network (FIG.25 ), a deep belief neural network (FIG. 26 ), a deep convolutionalneural network (FIG. 27 ), a deconvolutional neural network (FIG. 28 ),and a deep convolutional inverse graphics neural network (FIG. 29 ). Theplatform may also be associated with further neural net systems such asa generative adversarial neural network (FIG. 30 ), a liquid statemachine neural network (FIG. 31 ), an extreme learning machine neuralnetwork (FIG. 32 ), an echo state neural network (FIG. 33 ), a deepresidual neural network (FIG. 34 ), a Kohonen neural network (FIG. 35 ),a support vector machine neural network (FIG. 36 ), and a neural Turingmachine neural network (FIG. 37 ).

The foregoing neural networks may have a variety of nodes or neurons,which may perform a variety of functions on inputs, such as inputsreceived from sensors or other data sources, including other nodes.Functions may involve weights, features, feature vectors, and the like.Neurons may include perceptrons, neurons that mimic biological functions(such as of the human senses of touch, vision, taste, hearing, andsmell), and the like. Continuous neurons, such as with sigmoidalactivation, may be used in the context of various forms of neural net,such as where back propagation is involved.

In many embodiments, an expert system or neural network may be trained,such as by a human operator or supervisor, or based on a data set,model, or the like. Training may include presenting the neural networkwith one or more training data sets that represent values, such assensor data, event data, parameter data, and other types of data(including the many types described throughout this disclosure), as wellas one or more indicators of an outcome, such as an outcome of aprocess, an outcome of a calculation, an outcome of an event, an outcomeof an activity, or the like. Training may include training inoptimization, such as training a neural network to optimize one or moresystems based on one or more optimization approaches, such as Bayesianapproaches, parametric B ayes classifier approaches, k-nearest-neighborclassifier approaches, iterative approaches, interpolation approaches,Pareto optimization approaches, algorithmic approaches, and the like.Feedback may be provided in a process of variation and selection, suchas with a genetic algorithm that evolves one or more solutions based onfeedback through a series of rounds.

In embodiments, a plurality of neural networks may be deployed in acloud platform that receives data streams and other inputs collected(such as by mobile data collectors) in one or more energy edgeenvironments and transmitted to the cloud platform over one or morenetworks, including using network coding to provide efficienttransmission. In the cloud platform, optionally using massively parallelcomputational capability, a plurality of different neural networks ofvarious types (including modular forms, structure-adaptive forms,hybrids, and the like) may be used to undertake prediction,classification, control functions, and provide other outputs asdescribed in connection with expert systems disclosed throughout thisdisclosure. The different neural networks may be structured to competewith each other (optionally including use evolutionary algorithms,genetic algorithms, or the like), such that an appropriate type ofneural network, with appropriate input sets, weights, node types andfunctions, and the like, may be selected, such as by an expert system,for a specific task involved in a given context, workflow, environmentprocess, system, or the like.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a feed forwardneural network, which moves information in one direction, such as from adata input, like a data source related to at least one resource orparameter related to a transactional environment, such as any of thedata sources mentioned throughout this disclosure, through a series ofneurons or nodes, to an output. Data may move from the input nodes tothe output nodes, optionally passing through one or more hidden nodes,without loops. In embodiments, feed forward neural networks may beconstructed with various types of units, such as binary McCulloch-Pittsneurons, the simplest of which is a perceptron.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a capsule neuralnetwork, such as for prediction, classification, or control functionswith respect to a transactional environment, such as relating to one ormore of the machines and automated systems described throughout thisdisclosure.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a radial basisfunction (RBF) neural network, which may be preferred in some situationsinvolving interpolation in a multi-dimensional space (such as whereinterpolation is helpful in optimizing a multi-dimensional function,such as for optimizing a data marketplace as described here, optimizingthe efficiency or output of a power generation system, a factory system,or the like, or other situation involving multiple dimensions. Inembodiments, each neuron in the RBF neural network stores an examplefrom a training set as a “prototype.” Linearity involved in thefunctioning of this neural network offers RBF the advantage of nottypically suffering from problems with local minima or maxima.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a radial basisfunction (RBF) neural network, such as one that employs a distancecriterion with respect to a center (e.g., a Gaussian function). A radialbasis function may be applied as a replacement for a hidden layer, suchas a sigmoidal hidden layer transfer, in a multi-layer perceptron. AnRBF network may have two layers, such as where an input is mapped ontoeach RBF in a hidden layer. In embodiments, an output layer may comprisea linear combination of hidden layer values representing, for example, amean predicted output. The output layer value may provide an output thatis the same as or similar to that of a regression model in statistics.In classification problems, the output layer may be a sigmoid functionof a linear combination of hidden layer values, representing a posteriorprobability. Performance in both cases is often improved by shrinkagetechniques, such as ridge regression in classical statistics. Thiscorresponds to a prior belief in small parameter values (and thereforesmooth output functions) in a Bayesian framework. RBF networks may avoidlocal minima, because the only parameters that are adjusted in thelearning process are the linear mapping from hidden layer to outputlayer. Linearity ensures that the error surface is quadratic andtherefore has a single minimum. In regression problems, this may befound in one matrix operation. In classification problems, the fixednon-linearity introduced by the sigmoid output function may be handledusing an iteratively re-weighted least squares function or the like. RBFnetworks may use kernel methods such as support vector machines (SVM)and Gaussian processes (where the RBF is the kernel function). Anon-linear kernel function may be used to project the input data into aspace where the learning problem may be solved using a linear model.

In embodiments, an RBF neural network may include an input layer, ahidden layer, and a summation layer. In the input layer, one neuronappears in the input layer for each predictor variable. In the case ofcategorical variables, N−1 neurons are used, where N is the number ofcategories. The input neurons may, in embodiments, standardize the valueranges by subtracting the median and dividing by the interquartilerange. The input neurons may then feed the values to each of the neuronsin the hidden layer. In the hidden layer, a variable number of neuronsmay be used (determined by the training process). Each neuron mayconsist of a radial basis function that is centered on a point with asmany dimensions as a number of predictor variables. The spread (e.g.,radius) of the RBF function may be different for each dimension. Thecenters and spreads may be determined by training. When presented withthe vector of input values from the input layer, a hidden neuron maycompute a Euclidean distance of the test case from the neuron's centerpoint and then apply the RBF kernel function to this distance, such asusing the spread values. The resulting value may then be passed to thesummation layer. In the summation layer, the value coming out of aneuron in the hidden layer may be multiplied by a weight associated withthe neuron and may add to the weighted values of other neurons. This sumbecomes the output. For classification problems, one output is produced(with a separate set of weights and summation units) for each targetcategory. The value output for a category is the probability that thecase being evaluated has that category. In training of an RBF, variousparameters may be determined, such as the number of neurons in a hiddenlayer, the coordinates of the center of each hidden-layer function, thespread of each function in each dimension, and the weights applied tooutputs as they pass to the summation layer. Training may be used byclustering algorithms (such as k-means clustering), by evolutionaryapproaches, and the like.

In embodiments, a recurrent neural network may have a time-varying,real-valued (more than just zero or one) activation (output). Eachconnection may have a modifiable real-valued weight. Some of the nodesare called labeled nodes, some output nodes, and others hidden nodes.For supervised learning in discrete time settings, training sequences ofreal-valued input vectors may become sequences of activations of theinput nodes, one input vector at a time. At each time step, eachnon-input unit may compute its current activation as a nonlinearfunction of the weighted sum of the activations of all units from whichit receives connections. The system may explicitly activate (independentof incoming signals) some output units at certain time steps.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a self-organizingneural network, such as a Kohonen self organizing neural network, suchas for visualization of views of data, such as low-dimensional views ofhigh-dimensional data. The self-organizing neural network may applycompetitive learning to a set of input data, such as from one or moresensors or other data inputs from or associated with a transactionalenvironment, including any machine or component that relates to thetransactional environment. In embodiments, the self-organizing neuralnetwork may be used to identify structures in data, such as unlabeleddata, such as in data sensed from a range of data sources about orsensors in or about in a transactional environment, where sources of thedata are unknown (such as where events may be coming from any of a rangeof unknown sources). The self-organizing neural network may organizestructures or patterns in the data, such that they may be recognized,analyzed, and labeled, such as identifying market behavior structures ascorresponding to other events and signals.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a recurrent neuralnetwork, which may allow for a bi directional flow of data, such aswhere connected units (e.g., neurons or nodes) form a directed cycle.Such a network may be used to model or exhibit dynamic temporalbehavior, such as involved in dynamic systems, such as a wide variety ofthe automation systems, machines and devices described throughout thisdisclosure, such as an automated agent interacting with a marketplacefor purposes of collecting data, testing spot market transactions,execution transactions, and the like, where dynamic system behaviorinvolves complex interactions that a user may desire to understand,predict, control and/or optimize. For example, the recurrent neuralnetwork may be used to anticipate the state of a market, such as oneinvolving a dynamic process or action, such as a change in state of aresource that is traded in or that enables a marketplace oftransactional environment. In embodiments, the recurrent neural networkmay use internal memory to process a sequence of inputs, such as fromother nodes and/or from sensors and other data inputs from or about thetransactional environment, of the various types described herein. Inembodiments, the recurrent neural network may also be used for patternrecognition, such as for recognizing a machine, component, agent, orother item based on a behavioral signature, a profile, a set of featurevectors (such as in an audio file or image), or the like. In anon-limiting example, a recurrent neural network may recognize a shiftin an operational mode of a marketplace or machine by learning toclassify the shift from a training data set consisting of a stream ofdata from one or more data sources of sensors applied to or about one ormore resources.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a modular neuralnetwork, which may comprise a series of independent neural networks(such as ones of various types described herein) that are moderated byan intermediary. Each of the independent neural networks in the modularneural network may work with separate inputs, accomplishing sub tasksthat make up the task the modular network as whole is intended toperform. For example, a modular neural network may comprise a recurrentneural network for pattern recognition, such as to recognize what typeof machine or system is being sensed by one or more sensors that areprovided as input channels to the modular network and an RBF neuralnetwork for optimizing the behavior of the machine or system onceunderstood. The intermediary may accept inputs of each of the individualneural networks, process them, and create output for the modular neuralnetwork, such an appropriate control parameter, a prediction of state,or the like.

Combinations among any of the pairs, triplets, or larger combinations,of the various neural network types described herein, are encompassed bythe present disclosure. This may include combinations where an expertsystem uses one neural network for recognizing a pattern (e.g., apattern indicating a problem or fault condition) and a different neuralnetwork for self-organizing an activity or workflow based on therecognized pattern (such as providing an output governing autonomouscontrol of a system in response to the recognized condition or pattern).This may also include combinations where an expert system uses oneneural network for classifying an item (e.g., identifying a machine, acomponent, or an operational mode) and a different neural network forpredicting a state of the item (e.g., a fault state, an operationalstate, an anticipated state, a maintenance state, or the like). Modularneural networks may also include situations where an expert system usesone neural network for determining a state or context (such as a stateof a machine, a process, a work flow, a marketplace, a storage system, anetwork, a data collector, or the like) and a different neural networkfor self-organizing a process involving the state or context (e.g., adata storage process, a network coding process, a network selectionprocess, a data marketplace process, a power generation process, amanufacturing process, a refining process, a digging process, a boringprocess, or other process described herein).

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a physical neuralnetwork where one or more hardware elements is used to perform orsimulate neural behavior. In embodiments, one or more hardware neuronsmay be configured to stream voltage values, current values, or the likethat represent sensor data, such as to calculate information from analogsensor inputs representing energy consumption, energy production, or thelike, such as by one or more machines providing energy or consumingenergy for one or more transactions. One or more hardware nodes may beconfigured to stream output data resulting from the activity of theneural net. Hardware nodes, which may comprise one or more chips,microprocessors, integrated circuits, programmable logic controllers,application-specific integrated circuits, field-programmable gatearrays, or the like, may be provided to optimize the machine that isproducing or consuming energy, or to optimize another parameter of somepart of a neural net of any of the types described herein. Hardwarenodes may include hardware for acceleration of calculations (such asdedicated processors for performing basic or more sophisticatedcalculations on input data to provide outputs, dedicated processors forfiltering or compressing data, dedicated processors for de-compressingdata, dedicated processors for compression of specific file or datatypes (e.g., for handling image data, video streams, acoustic signals,thermal images, heat maps, or the like), and the like. A physical neuralnetwork may be embodied in a data collector, including one that may bereconfigured by switching or routing inputs in varying configurations,such as to provide different neural net configurations within the datacollector for handling different types of inputs (with the switching andconfiguration optionally under control of an expert system, which mayinclude a software-based neural net located on the data collector orremotely). A physical, or at least partially physical, neural networkmay include physical hardware nodes located in a storage system, such asfor storing data within a machine, a data storage system, a distributedledger, a mobile device, a server, a cloud resource, or in atransactional environment, such as for accelerating input/outputfunctions to one or more storage elements that supply data to or takedata from the neural net. A physical, or at least partially physical,neural network may include physical hardware nodes located in a network,such as for transmitting data within, to or from an energy edgeenvironment, such as for accelerating input/output functions to one ormore network nodes in the net, accelerating relay functions, or thelike. In embodiments of a physical neural network, an electricallyadjustable resistance material may be used for emulating the function ofa neural synapse. In embodiments, the physical hardware emulates theneurons, and software emulates the neural network between the neurons.In embodiments, neural networks complement conventional algorithmiccomputers. They are versatile and may be trained to perform appropriatefunctions without the need for any instructions, such as classificationfunctions, optimization functions, pattern recognition functions,control functions, selection functions, evolution functions, and others.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a multilayeredfeed forward neural network, such as for complex pattern classificationof one or more items, phenomena, modes, states, or the like. Inembodiments, a multilayered feed forward neural network may be trainedby an optimization technique, such as a genetic algorithm, such as toexplore a large and complex space of options to find an optimum, ornear-optimum, global solution. For example, one or more geneticalgorithms may be used to train a multilayered feed forward neuralnetwork to classify complex phenomena, such as to recognize complexoperational modes of machines, such as modes involving complexinteractions among machines (including interference effects, resonanceeffects, and the like), modes involving non-linear phenomena, modesinvolving critical faults, such as where multiple, simultaneous faultsoccur, making root cause analysis difficult, and others. In embodiments,a multilayered feed forward neural network may be used to classifyresults from monitoring of a marketplace, such as monitoring systems,such as automated agents, that operate within the marketplace, as wellas monitoring resources that enable the marketplace, such as computing,networking, energy, data storage, energy storage, and other resources.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a feed-forward,back-propagation multi-layer perceptron (MLP) neural network, such asfor handling one or more remote sensing applications, such as for takinginputs from sensors distributed throughout various transactionalenvironments. In embodiments, the MLP neural network may be used forclassification of energy edge environments and resource environments,such as spot markets, forward markets, energy markets, renewable energycredit (REC) markets, networking markets, advertising markets, spectrummarkets, ticketing markets, rewards markets, compute markets, and othersmentioned throughout this disclosure, as well as physical resources andenvironments that produce them, such as energy resources (includingrenewable energy environments, mining environments, explorationenvironments, drilling environments, and the like, includingclassification of geological structures (including underground featuresand above ground features), classification of materials (includingfluids, minerals, metals, and the like), and other problems. This mayinclude fuzzy classification. In embodiments, methods and systemsdescribed herein that involve an expert system or self-organizationcapability may use a structure-adaptive neural network, where thestructure of a neural network is adapted, such as based on a rule, asensed condition, a contextual parameter, or the like. For example, if aneural network does not converge on a solution, such as classifying anitem or arriving at a prediction, when acting on a set of inputs aftersome amount of training, the neural network may be modified, such asfrom a feed forward neural network to a recurrent neural network, suchas by switching data paths between some subset of nodes fromunidirectional to bi directional data paths. The structure adaptationmay occur under control of an expert system, such as to triggeradaptation upon occurrence of a trigger, rule or event, such asrecognizing occurrence of a threshold (such as an absence of aconvergence to a solution within a given amount of time) or recognizinga phenomenon as requiring different or additional structure (such asrecognizing that a system is varying dynamically or in a non-linearfashion). In one non-limiting example, an expert system may switch froma simple neural network structure like a feed forward neural network toa more complex neural network structure like a recurrent neural network,a convolutional neural network, or the like upon receiving an indicationthat a continuously variable transmission is being used to drive agenerator, turbine, or the like in a system being analyzed.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an autoencoder,autoassociator or Diabolo neural network, which may be similar to amultilayer perceptron (MLP) neural network, such as where there may bean input layer, an output layer and one or more hidden layers connectingthem. However, the output layer in the auto-encoder may have the samenumber of units as the input layer, where the purpose of the MLP neuralnetwork is to reconstruct its own inputs (rather than just emitting atarget value). Therefore, the auto encoders may operate as anunsupervised learning model. An auto encoder may be used, for example,for unsupervised learning of efficient codings, such as fordimensionality reduction, for learning generative models of data, andthe like. In embodiments, an auto-encoding neural network may be used toself-learn an efficient network coding for transmission of analog sensordata from a machine over one or more networks or of digital data fromone or more data sources. In embodiments, an auto-encoding neuralnetwork may be used to self-learn an efficient storage approach forstorage of streams of data.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a probabilisticneural network (PNN), which, in embodiments, may comprise a multi-layer(e.g., four-layer) feed forward neural network, where layers may includeinput layers, hidden layers, pattern/summation layers and an outputlayer. In an embodiment of a PNN algorithm, a parent probabilitydistribution function (PDF) of each class may be approximated, such asby a Parzen window and/or a non-parametric function. Then, using the PDFof each class, the class probability of a new input is estimated, andBayes' rule may be employed, such as to allocate it to the class withthe highest posterior probability. A PNN may embody a Bayesian networkand may use a statistical algorithm or analytic technique, such asKernel Fisher discriminant analysis technique. The PNN may be used forclassification and pattern recognition in any of a wide range ofembodiments disclosed herein. In one non-limiting example, aprobabilistic neural network may be used to predict a fault condition ofan engine based on collection of data inputs from sensors andinstruments for the engine.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a time delayneural network (TDNN), which may comprise a feed forward architecturefor sequential data that recognizes features independent of sequenceposition. In embodiments, to account for time shifts in data, delays areadded to one or more inputs, or between one or more nodes, so thatmultiple data points (from distinct points in time) are analyzedtogether. A time delay neural network may form part of a larger patternrecognition system, such as using a perceptron network. In embodiments,a TDNN may be trained with supervised learning, such as where connectionweights are trained with back propagation or under feedback. Inembodiments, a TDNN may be used to process sensor data from distinctstreams, such as a stream of velocity data, a stream of accelerationdata, a stream of temperature data, a stream of pressure data, and thelike, where time delays are used to align the data streams in time, suchas to help understand patterns that involve understanding of the variousstreams (e.g., changes in price patterns in spot or forward markets).

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a convolutionalneural network (referred to in some cases as a CNN, a ConvNet, a shiftinvariant neural network, or a space invariant neural network), whereinthe units are connected in a pattern similar to the visual cortex of thehuman brain. Neurons may respond to stimuli in a restricted region ofspace, referred to as a receptive field. Receptive fields may partiallyoverlap, such that they collectively cover the entire (e.g., visual)field. Node responses may be calculated mathematically, such as by aconvolution operation, such as using multilayer perceptrons that useminimal preprocessing. A convolutional neural network may be used forrecognition within images and video streams, such as for recognizing atype of machine in a large environment using a camera system disposed ona mobile data collector, such as on a drone or mobile robot. Inembodiments, a convolutional neural network may be used to provide arecommendation based on data inputs, including sensor inputs and othercontextual information, such as recommending a route for a mobile datacollector. In embodiments, a convolutional neural network may be usedfor processing inputs, such as for natural language processing ofinstructions provided by one or more parties involved in a workflow inan environment. In embodiments, a convolutional neural network may bedeployed with a large number of neurons (e.g., 100,000, 500,000 ormore), with multiple (e.g., 4, 5, 6 or more) layers, and with many(e.g., millions) of parameters. A convolutional neural net may use oneor more convolutional nets.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a regulatoryfeedback network, such as for recognizing emergent phenomena (such asnew types of behavior not previously understood in a transactionalenvironment).

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a self-organizingmap (SOM), involving unsupervised learning. A set of neurons may learnto map points in an input space to coordinates in an output space. Theinput space may have different dimensions and topology from the outputspace, and the SOM may preserve these while mapping phenomena intogroups.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a learning vectorquantization neural net (LVQ). Prototypical representatives of theclasses may parameterize, together with an appropriate distance measure,in a distance-based classification scheme.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an echo statenetwork (ESN), which may comprise a recurrent neural network with asparsely connected, random hidden layer. The weights of output neuronsmay be changed (e.g., the weights may be trained based on feedback). Inembodiments, an ESN may be used to handle time series patterns, such as,in an example, recognizing a pattern of events associated with a market,such as the pattern of price changes in response to stimuli.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a Bi-directional,recurrent neural network (BRNN), such as using a finite sequence ofvalues (e.g., voltage values from a sensor) to predict or label eachelement of the sequence based on both the past and the future context ofthe element. This may be done by adding the outputs of two RNNs, such asone processing the sequence from left to right, the other one from rightto left. The combined outputs are the predictions of target signals,such as ones provided by a teacher or supervisor. A bi-directional RNNmay be combined with a long short-term memory RNN.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a hierarchical RNNthat connects elements in various ways to decompose hierarchicalbehavior, such as into useful subprograms. In embodiments, ahierarchical RNN may be used to manage one or more hierarchicaltemplates for data collection in a transactional environment.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a stochasticneural network, which may introduce random variations into the network.Such random variations may be viewed as a form of statistical sampling,such as Monte Carlo sampling.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a genetic scalerecurrent neural network. In such embodiments, an RNN (often an LSTM) isused where a series is decomposed into a number of scales where everyscale informs the primary length between two consecutive points. A firstorder scale consists of a normal RNN, a second order consists of allpoints separated by two indices and so on. The Nth order RNN connectsthe first and last node. The outputs from all the various scales may betreated as a committee of members, and the associated scores may be usedgenetically for the next iteration.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a committee ofmachines (CoM), comprising a collection of different neural networksthat together “vote” on a given example. Because neural networks maysuffer from local minima, starting with the same architecture andtraining, but using randomly different initial weights often givesdifferent results. A CoM tends to stabilize the result.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an associativeneural network (ASNN), such as involving an extension of a committee ofmachines that combines multiple feed forward neural networks and ak-nearest neighbor technique. It may use the correlation betweenensemble responses as a measure of distance amid the analyzed cases forthe kNN. This corrects the bias of the neural network ensemble. Anassociative neural network may have a memory that may coincide with atraining set. If new data become available, the network instantlyimproves its predictive ability and provides data approximation(self-learns) without retraining. Another important feature of ASNN isthe possibility to interpret neural network results by analysis ofcorrelations between data cases in the space of models.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an instantaneouslytrained neural network (ITNN), where the weights of the hidden and theoutput layers are mapped directly from training vector data.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a spiking neuralnetwork, which may explicitly consider the timing of inputs. The networkinput and output may be represented as a series of spikes (such as adelta function or more complex shapes). SNNs may process information inthe time domain (e.g., signals that vary over time, such as signalsinvolving dynamic behavior of markets or transactional environments).They are often implemented as recurrent networks.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a dynamic neuralnetwork that addresses nonlinear multivariate behavior and includeslearning of time-dependent behavior, such as transient phenomena anddelay effects. Transients may include behavior of shifting marketvariables, such as prices, available quantities, availablecounterparties, and the like.

In embodiments, cascade correlation may be used as an architecture andsupervised learning algorithm, supplementing adjustment of the weightsin a network of fixed topology. Cascade-correlation may begin with aminimal network, then automatically trains and add new hidden units oneby one, creating a multi-layer structure. Once a new hidden unit hasbeen added to the network, its input-side weights may be frozen. Thisunit then becomes a permanent feature-detector in the network, availablefor producing outputs or for creating other, more complex featuredetectors. The cascade-correlation architecture may learn quickly,determine its own size and topology, and retain the structures it hasbuilt even if the training set changes and requires no back-propagation.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a neuro-fuzzynetwork, such as involving a fuzzy inference system in the body of anartificial neural network. Depending on the type, several layers maysimulate the processes involved in a fuzzy inference, such asfuzzification, inference, aggregation and defuzzification. Embedding afuzzy system in a general structure of a neural net as the benefit ofusing available training methods to find the parameters of a fuzzysystem.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a compositionalpattern-producing network (CPPN), such as a variation of an associativeneural network (ANN) that differs the set of activation functions andhow they are applied. While typical ANNs often contain only sigmoidfunctions (and sometimes Gaussian functions), CPPNs may include bothtypes of functions and many others. Furthermore, CPPNs may be appliedacross the entire space of possible inputs, so that they may represent acomplete image. Since they are compositions of functions, CPPNs ineffect encode images at infinite resolution and may be sampled for aparticular display at whatever resolution is optimal.

This type of network may add new patterns without re-training. Inembodiments, methods and systems described herein that involve an expertsystem or self-organization capability may use a one-shot associativememory network, such as by creating a specific memory structure, whichassigns each new pattern to an orthogonal plane using adjacentlyconnected hierarchical arrays.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a hierarchicaltemporal memory (HTM) neural network, such as involving the structuraland algorithmic properties of the neocortex. HTM may use a biomimeticmodel based on memory-prediction theory. HTM may be used to discover andinfer the high-level causes of observed input patterns and sequences.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a holographicassociative memory (HAM) neural network, which may comprise an analog,correlation-based, associative, stimulus-response system. Informationmay be mapped onto the phase orientation of complex numbers. The memoryis effective for associative memory tasks, generalization and patternrecognition with changeable attention.

Quantum Computing Service

FIG. 38 illustrates an example quantum computing system 3800 accordingto some embodiments of the present disclosure. In embodiments, thequantum computing system 3800 provides a framework for providing a setof quantum computing services to one or more quantum computing clients.In some embodiments, the quantum computing system 3800 framework may beat least partially replicated in respective quantum computing clients.In these embodiments, an individual client may include some or all ofthe capabilities of the quantum computing system 3800, whereby thequantum computing system 3800 is adapted for the specific functionsperformed by the subsystems of the quantum computing client.Additionally, or alternatively, in some embodiments, the quantumcomputing system 3800 may be implemented as a set of microservices, suchthat different quantum computing clients may leverage the quantumcomputing system 3800 via one or more APIs exposed to the quantumcomputing clients. In these embodiments, the quantum computing system3800 may be configured to perform various types of quantum computingservices that may be adapted for different quantum computing clients. Ineither of these configurations, a quantum computing client may provide arequest to the quantum computing system 3800, whereby the request is toperform a specific task (e.g., an optimization). In response, thequantum computing system 3800 executes the requested task and returns aresponse to the quantum computing client.

Referring to FIG. 38 , in some embodiments, the quantum computing system3800 may include a quantum adapted services library 3802, a quantumgeneral services library 3804, a quantum data services library 3806, aquantum computing engine library 3808, a quantum computing configurationservice 3810, a quantum computing execution system 3812, and quantumcomputing API interface 3814.

In embodiments, the quantum computing engine library 3808 includesquantum computing engine configurations 3816 and quantum computingprocess modules 3818 based on various supported quantum models. Inembodiments, the quantum computing system 3800 may support manydifferent quantum models, including, but not limited to, the quantumcircuit model, quantum Turing machine, adiabatic quantum computer,spintronic computing system (such as using spin-orbit coupling togenerate spin-polarized electronic states in non-magnetic solids, suchas ones using diamond materials), one-way quantum computer, quantumannealing, and various quantum cellular automata. Under the quantumcircuit model, quantum circuits may be based on the quantum bit, or“qubit”, which is somewhat analogous to the bit in classicalcomputation. Qubits may be in a 1 or 0 quantum state or they may be in asuperposition of the 1 and 0 states. However, when qubits have measuredthe result of a measurement, qubits will always be in is always either a1 or 0 quantum state. The probabilities related to these two outcomesdepend on the quantum state that the qubits were in immediately beforethe measurement. Computation is performed by manipulating qubits withquantum logic gates, which are somewhat analogous to classical logicgates.

In embodiments, the quantum computing system 3800 may be physicallyimplemented using an analog approach or a digital approach. Analogapproaches may include, but are not limited to, quantum simulation,quantum annealing, and adiabatic quantum computation. In embodiments,digital quantum computers use quantum logic gates for computation. Bothanalog and digital approaches may use quantum bits, or qubits.

In embodiments, the quantum computing system 3800 includes a quantumannealing module 3820 wherein the quantum annealing module may beconfigured to find the global minimum or maximum of a given objectivefunction over a given set of candidate solutions (e.g., candidatestates) using quantum fluctuations. As used herein, quantum annealingmay refer to a meta-procedure for finding a procedure that identifies anabsolute minimum or maximum, such as a size, length, cost, time,distance or other measure, from within a possibly very large, butfinite, set of possible solutions using quantum fluctuation-basedcomputation instead of classical computation. The quantum annealingmodule 3820 may be leveraged for problems where the search space isdiscrete (e.g., combinatorial optimization problems) with many localminima, such as finding the ground state of a spin glass or thetraveling salesman problem.

In embodiments, the quantum annealing module 3820 starts from aquantum-mechanical superposition of all possible states (candidatestates) with equal weights. The quantum annealing module 3820 may thenevolve, such as following the time-dependent Schrödinger equation, anatural quantum-mechanical evolution of systems (e.g., physical systems,logical systems, or the like). In embodiments, the amplitudes of allcandidate states change, realizing quantum parallelism according to thetime-dependent strength of the transverse field, which causes quantumtunneling between states. If the rate of change of the transverse fieldis slow enough, the quantum annealing module 3820 may stay close to theground state of the instantaneous Hamiltonian. If the rate of change ofthe transverse field is accelerated, the quantum annealing module 3820may leave the ground state temporarily but produce a higher likelihoodof concluding in the ground state of the final problem energy state orHamiltonian.

In embodiments, the quantum computing system 3800 may includearbitrarily large numbers of qubits and may transport ions to spatiallydistinct locations in an array of ion traps, building large, entangledstates via photonically connected networks of remotely entangled ionchains.

In some implementations, the quantum computing system 3800 includes atrapped ion computer module 3822, which may be a quantum computer thatapplies trapped ions to solve complex problems. Trapped ion computermodule 3822 may have low quantum decoherence and may be able toconstruct large solution states. Ions, or charged atomic particles, maybe confined and suspended in free space using electromagnetic fields.Qubits are stored in stable electronic states of each ion, and quantuminformation may be transferred through the collective quantized motionof the ions in a shared trap (interacting through the Coulomb force).Lasers may be applied to induce coupling between the qubit states (forsingle-qubit operations) or coupling between the internal qubit statesand the external motional states (for entanglement between qubits).

In some embodiments of the invention, a traditional computer, includinga processor, memory, and a graphical user interface (GUI), may be usedfor designing, compiling, and providing output from the execution andthe quantum computing system 3800 may be used for executing the machinelanguage instructions. In some embodiments of the invention, the quantumcomputing system 3800 may be simulated by a computer program executed bythe traditional computer. In such embodiments, a superposition of statesof the quantum computing system 3800 can be prepared based on input fromthe initial conditions. Since the initialization operation available ina quantum computer can only initialize a qubit to either the |0> or |1>state, initialization to a superposition of states is physicallyunrealistic. For simulation purposes, however, it may be useful tobypass the initialization process and initialize the quantum computingsystem 3800 directly.

In some embodiments, the quantum computing system 3800 provides variousquantum data services, including quantum input filtering, quantum outputfiltering, quantum application filtering, and a quantum database engine.

In embodiments, the quantum computing system 3800 may include a quantuminput filtering service 3824. In embodiments, quantum input filteringservice 3824 may be configured to select whether to run a model on thequantum computing system 3800 or to run the model on a classic computingsystem. In some embodiments, quantum input filtering service 3824 mayfilter data for later modeling on a classic computer. In embodiments,the quantum computing system 3800 may provide input to traditionalcompute platforms while filtering out unnecessary information fromflowing into distributed systems. In some embodiments, the platform 3800may trust through filtered specified experiences for intelligent agents.

In embodiments, a system in the system of systems may include a model orsystem for automatically determining, based on a set of inputs, whetherto deploy quantum computational or quantum algorithmic resources to anactivity, whether to deploy traditional computational resources andalgorithms, or whether to apply a hybrid or combination of them. Inembodiments, inputs to a model or automation system may include demandinformation, supply information, financial data, energy costinformation, capital costs for computational resources, developmentcosts (such as for algorithms), energy costs, operational costs(including labor and other costs), performance information on availableresources (quantum and traditional), and any of the many other data setsthat may be used to simulate (such as using any of a wide variety ofsimulation techniques described herein and/or in the documentsincorporated herein by refence) and/or predict the difference in outcomebetween a quantum-optimized result and a non-quantum-optimized result. Amachine learned model (including in a DPANN system) may be trained, suchas by deep learning on outcomes or by a data set from human expertdecisions, to determine what set of resources to deploy given the inputdata for a given request. The model may itself be deployed on quantumcomputational resources and/or may use quantum algorithms, such asquantum annealing, to determine whether, where and when to use quantumsystems, conventional systems, and/or hybrids or combinations.

In some embodiments of the invention, the quantum computing system 3800may include a quantum output filtering service 3826. In embodiments, thequantum output filtering service 3826 may be configured to select asolution from solutions of multiple neural networks. For example,multiple neural networks may be configured to generate solutions to aspecific problem and the quantum output filtering service 3826 mayselect the best solution from the set of solutions.

In some embodiments, the quantum computing system 3800 connects anddirects a neural network development or selection process. In thisembodiment, the quantum computing system 3800 may directly program theweights of a neural network such that the neural network gives thedesired outputs. This quantum-programmed neural network may then operatewithout the oversight of the quantum computing system 3800 but willstill be operating within the expected parameters of the desiredcomputational engine.

In embodiments, the quantum computing system 3800 includes a quantumdatabase engine 3828. In embodiments, the quantum database engine 3828is configured with in-database quantum algorithm execution. Inembodiments, a quantum query language may be employed to query thequantum database engine 3828. In some embodiments, the quantum databaseengine may have an embedded policy engine 3830 for prioritization and/orallocation of quantum workflows, including prioritization of queryworkloads, such as based on overall priority as well as the comparativeadvantage of using quantum computing resources versus others. Inembodiments, quantum database engine 3828 may assist with therecognition of entities by establishing a single identity for that isvalid across interactions and touchpoints. The quantum database engine3828 may be configured to perform optimization of data matching andintelligent traditional compute optimization to match individual dataelements. The quantum computing system 3800 may include a quantum dataobfuscation system for obfuscating data.

The quantum computing system 3800 may include, but is not limited to,analog quantum computers, digital computers, and/or error-correctedquantum computers. Analog quantum computers may directly manipulate theinteractions between qubits without breaking these actions intoprimitive gate operations. In embodiments, quantum computers that mayrun analog machines include, but are not limited to, quantum annealers,adiabatic quantum computers, and direct quantum simulators. The digitalcomputers may operate by carrying out an algorithm of interest usingprimitive gate operations on physical qubits. Error-corrected quantumcomputers may refer to a version of gate-based quantum computers mademore robust through the deployment of quantum error correction (QEC),which enables noisy physical qubits to emulate stable logical qubits sothat the computer behaves reliably for any computation. Further, quantuminformation products may include, but are not limited to, computingpower, quantum predictions, and quantum inventions.

In some embodiments, the quantum computing system 3800 is configured asan engine that may be used to optimize traditional computers, integratedata from multiple sources into a decision-making process, and the like.The data integration process may involve real-time capture andmanagement of interaction data by a wide range of tracking capabilities,both directly and indirectly related to value chain network activities.In embodiments, the quantum computing system 3800 may be configured toaccept cookies, email addresses and other contact data, social mediafeeds, news feeds, event and transaction log data (including transactionevents, network events, computational events, and many others), eventstreams, results of web crawling, distributed ledger information(including blockchain updates and state information), results fromdistributed or federated queries of data sources, streams of data fromchat rooms and discussion forums, and many others.

In embodiments, the quantum computing system 3800 includes a quantumregister having a plurality of qubits. Further, the quantum computingsystem 3800 may include a quantum control system for implementing thefundamental operations on each of the qubits in the quantum register anda control processor for coordinating the operations required.

In embodiments, the quantum computing system 3800 is configured tooptimize the pricing of a set of goods or services. In embodiments, thequantum computing system 3800 may utilize quantum annealing to provideoptimized pricing. In embodiments, the quantum computing system 3800 mayuse q-bit based computational methods to optimize pricing.

In embodiments, the quantum computing system 3800 is configured toautomatically discover smart contract configuration opportunities.Automated discovery of smart contract configuration opportunities may bebased on published APIs to marketplaces and machine learning (e.g., byrobotic process automation (RPA) of stakeholder, asset, and transactiontypes.

In embodiments, quantum-established or other blockchain-enabled smartcontracts enable frequent transactions occurring among a network ofparties, and manual or duplicative tasks are performed by counterpartiesfor each transaction. The quantum-established or other blockchain actsas a shared database to provide a secure, single source of truth, andsmart contracts automate approvals, calculations, and other transactingactivities that are prone to lag and error. Smart contracts may usesoftware code to automate tasks, and in some embodiments, this softwarecode may include quantum code that enables extremely sophisticatedoptimized results.

In embodiments, the quantum computing system 3800 or other system in thesystem of systems may include a quantum-enabled or other riskidentification module that is configured to perform risk identificationand/or mitigation. The steps that may be taken by the riskidentification module may include, but are not limited to, riskidentification, impact assessment, and the like. In some embodiments,the risk identification module determines a risk type from a set of risktypes. In embodiments, risks may include, but are not limited to,preventable, strategic, and external risks. Preventable risks may referto risks that come from within and that can usually be managed on arule-based level, such as employing operational procedures monitoringand employee and manager guidance and instruction. Strategy risks mayrefer to those risks that are taken on voluntarily to achieve greaterrewards. External risks may refer to those risks that originate outsideand are not in the businesses' control (such as natural disasters).External risks are not preventable or desirable. In embodiments, therisk identification module can determine a predicted cost for manycategories of risk. The risk identification module may perform acalculation of current and potential impact on an overall risk profile.In embodiments, the risk identification module may determine theprobability and significance of certain events. Additionally, oralternatively, the risk identification module may be configured toanticipate events.

In embodiments, the quantum computing system 3800 or other system of theplatform 3800 is configured for graph clustering analysis for anomalyand fraud detection.

In some embodiments, the quantum computing system 3800 includes aquantum prediction module, which is configured to generate predictions.Furthermore, the quantum prediction module may construct classicalprediction engines to further generate predictions, reducing the needfor ongoing quantum calculation costs, which, can be substantialcompared to traditional computers.

In embodiments, the quantum computing system 3800 may include a quantumprincipal component analysis (QPCA) algorithm that may process inputvector data if the covariance matrix of the data is efficientlyobtainable as a density matrix, under specific assumptions about thevectors given in the quantum mechanical form. It may be assumed that theuser has quantum access to the training vector data in a quantum memory.Further, it may be assumed that each training vector is stored in thequantum memory in terms of its difference from the class means. TheseQPCA algorithms can then be applied to provide for dimension reductionusing the calculational benefits of a quantum method.

In embodiments, the quantum computing system 3800 is configured forgraph clustering analysis for certified randomness for proof-of-stakeblockchains. Quantum cryptographic schemes may make use of quantummechanics in their designs, which enables such schemes to rely onpresumably unbreakable laws of physics for their security. The quantumcryptography schemes may be information-theoretically secure such thattheir security is not based on any non-fundamental assumptions. In thedesign of blockchain systems, information-theoretic security is notproven. Rather, classical blockchain technology typically relies onsecurity arguments that make assumptions about the limitations ofattackers' resources.

In embodiments, the quantum computing system 3800 is configured fordetecting adversarial systems, such as adversarial neural networks,including adversarial convolutional neural networks. For example, thequantum computing system 3800 or other systems of the platform 3800 maybe configured to detect fake trading patterns.

In embodiments, the quantum computing system 3800 includes a quantumcontinual learning (QCL) system 3832, wherein the QCL system 3832 learnscontinuously and adaptively about the external world, enabling theautonomous incremental development of complex skills and knowledge byupdating a quantum model to account for different tasks and datadistributions. The QCL system 3832 operates on a realistic time scalewhere data and/or tasks become available only during operation. Previousquantum states can be superimposed into the quantum engine to providethe capacity for QCL. Because the QCL system 3832 is not constrained toa finite number of variables that can be processed deterministically, itcan continuously adapt to future states, producing a dynamic continuallearning capability. The QCL system 3832 may have applications wheredata distributions stay relatively static, but where data iscontinuously being received. For example, the QCL system 3832 may beused in quantum recommendation applications or quantum anomaly detectionsystems where data is continuously being received and where the quantummodel is continuously refined to provide for various outcomes,predictions, and the like. QCL enables asynchronous alternate trainingof tasks and only updates the quantum model on the real-time dataavailable from one or more streaming sources at a particular moment.

In embodiments, the QCL system 3832 operates in a complex environment inwhich the target data keeps changing based on a hidden variable that isnot controlled. In embodiments, the QCL system 3832 can scale in termsof intelligence while processing increasing amounts of data and whilemaintaining a realistic number of quantum states. The QCL system 3832applies quantum methods to drastically reduce the requirement forstorage of historic data while allowing the execution of continuouscomputations to provide for detail-driven optimal results. Inembodiments, a QCL system 3832 is configured for unsupervised streamingperception data since it continually updates the quantum model with newavailable data.

In embodiments, QCL system 3832 enables multi-modal-multi-task quantumlearning. The QCL system 3832 is not constrained to a single stream ofperception data but allows for many streams of perception data fromdifferent sensors and input modalities. In embodiments, the QCL system3832 can solve multiple tasks by duplicating the quantum state andexecuting computations on the duplicate quantum environment. A keyadvantage to QCL is that the quantum model does not need to be retrainedon historic data, as the superposition state holds information relatingto all prior inputs. Multi-modal and multi-task quantum learning enhancequantum optimization since it endows quantum machines with reasoningskills through the application of vast amounts of state information.

In embodiments, the quantum computing system 3800 supports quantumsuperposition, or the ability of a set of states to be overlaid into asingle quantum environment.

In embodiments, the quantum computing system 3800 supports quantumteleportation. For example, information may be passed between photons onchipsets even if the photons are not physically linked.

In embodiments, the quantum computing system 3800 may include a quantumtransfer pricing system. Quantum transfer pricing allows for theestablishment of prices for the goods and/or services exchanged betweensubsidiaries, affiliates, or commonly controlled companies that are partof a larger enterprise and may be used to provide tax savings forcorporations. In embodiments, solving a transfer pricing probleminvolves testing the elasticities of each system in the system ofsystems with a set of tests. In these embodiments, the testing may bedone in periodic batches and then may be iterated. As described herein,transfer pricing may refer to the price that one division in a companycharges another division in that company for goods and services.

In embodiments, the quantum transfer pricing system consolidates allfinancial data related to transfer pricing on an ongoing basisthroughout the year for all entities of an organization wherein theconsolidation involves applying quantum entanglement to overlay datainto a single quantum state. In embodiments, the financial data mayinclude profit data, loss data, data from intercompany invoices(potentially including quantities and prices), and the like.

In embodiments, the quantum transfer pricing system may interface with areporting system that reports segmented profit and loss, transactionmatrices, tax optimization results, and the like based on superpositiondata. In embodiments, the quantum transfer pricing system automaticallygenerates forecast calculations and assesses the expected local profitsfor any set of quantum states.

In embodiments, the quantum transfer pricing system may integrate with asimulation system for performing simulations. Suggested optimal valuesfor new product prices can be discussed cross-border via integratedquantum workflows and quantum teleportation communicated states.

In embodiments, quantum transfer pricing may be used to proactivelycontrol the distribution of profits within a multi-national enterprise(MNE), for example, during the course of a calendar year, enabling theentities to achieve arms-length profit ranges for each type oftransaction.

In embodiments, the QCL system 3832 may use a number of methods tocalculate quantum transfer pricing, including the quantum comparableuncontrolled price (QCUP) method, the quantum cost plus percent method(QCPM), the quantum resale price method (QRPM), the quantum transactionnet margin method (QTNM), and the quantum profit-split method.

The QCUP method may apply quantum calculations to find comparabletransactions made between related and unrelated organizations,potentially through the sharing of quantum superposition data. Bycomparing the price of goods and/or services in an intercompanytransaction with the price used by independent parties through theapplication of a quantum comparison engine, a benchmark price may bedetermined.

The QCPM method may compare the gross profit to the cost of sales, thusmeasuring the cost-plus mark-up (the actual profit earned from theproducts). Once this mark-up is determined, it should be equal to what athird party would make for a comparable transaction in a comparablecontext with similar external market conditions. In embodiments, thequantum engine may simulate the external market conditions.

The QRPM method looks at groups of transactions rather than individualtransactions and is based on the gross margin or difference between theprice at which a product is purchased and the price at which it is soldto a third party. In embodiments, the quantum engine may be applied tocalculate the price differences and to record the transactions in thesuperposition system.

The QTNM method is based on the net profit of a controlled transactionrather than comparable external market pricing. The calculation of thenet profit is accomplished through a quantum engine that can consider awide variety of factors and solve optimally for the product price. Thenet profit may then be compared with the net profit of independententerprises, potentially using quantum teleportation.

The quantum profit-split method may be used when two related companieswork on the same business venture, but separately. In theseapplications, the quantum transfer pricing is based on profit. Thequantum profit-split method applies quantum calculations to determinehow the profit associated with a particular transaction would have beendivided between the independent parties involved.

In embodiments, the quantum computing system 3800 may leverage one orartificial networks to fulfill the request of a quantum computingclient. For example, the quantum computing system 3800 may leverage aset of artificial neural networks to identify patterns in images (e.g.,using image data from a liquid lens system), perform binary matrixfactorization, perform topical content targeting, performsimilarity-based clustering, perform collaborative filtering, performopportunity mining, or the like.

In embodiments, the system of systems may include a hybrid computingallocation system for prioritization and allocation of quantum computingresources and traditional computing resources. In embodiments, theprioritization and allocation of quantum computing resources andtraditional computing resources may be measure-based (e.g., measuringthe extent of the advantage of the quantum resource relative to otheravailable resources), cost-based, optimality-based, speed-based,impact-based, or the like. In some embodiments the hybrid computingallocation system is configured to perform time-division multiplexingbetween the quantum computing system 3800 and a traditional computingsystem. In embodiments, the hybrid computing allocation system mayautomatically track and report on the allocation of computationalresources, the availability of computational resources, the cost ofcomputational resources, and the like.

In embodiments, the quantum computing system 3800 may be leveraged forqueue optimization for utilization of quantum computing resources,including context-based queue optimizations.

In embodiments, the quantum computing system 3800 may supportquantum-computation-aware location-based data caching.

In embodiments, the quantum computing system 3800 may be leveraged foroptimization of various system resources in the system of systems,including the optimization of quantum computing resources, traditionalcomputing resources, energy resources, human resources, robotic fleetresources, smart container fleet resources, I/O bandwidth, storageresources, network bandwidth, attention resources, or the like.

The quantum computing system 3800 may be implemented where a completerange of capabilities are available to or as part of any configuredservice. Configured quantum computing services may be configured withsubsets of these capabilities to perform specific predefined function,produce newly defined functions, or various combinations of both.

FIG. 39 illustrates quantum computing service request handling accordingto some embodiments of the present disclosure. A directed quantumcomputing request 3902 may come from one or more quantum-aware devicesor stack of devices, where the request is for known applicationconfigured with specific quantum instance(s), quantum computingengine(s), or other quantum computing resources, and where dataassociated with the request may be preprocessed or otherwise optimizedfor use with quantum computing.

A general quantum computing request 3904 may come from any system in thesystem of systems or configured service, where the requestor hasdetermined that quantum computing resources may provide additional valueor other improved outcomes. Improved outcomes may also be suggested bythe quantum computing service in association with some form ofmonitoring and analysis. For a general quantum computing request 3904,input data may not be structured or formatted as necessary for quantumcomputing.

In embodiments, external data requests 3906 may include any availabledata that may be necessary for training new quantum instances. Thesources of such requests could be public data, sensors, ERP systems, andmany others.

Incoming operating requests and associated data may be analyzed using astandardized approach that identifies one or more possible sets of knownquantum instances, quantum computing engines, or other quantum computingresources that may be applied to perform the requested operation(s).Potential existing sets may be identified in the quantum set library3908.

In embodiments, the quantum computing system 3800 includes a quantumcomputing configuration service 3810. The quantum computingconfiguration service may work alone or with the intelligence service3834 to select a best available configuration using a resource andpriority analysis that also includes the priority of the requestor. Thequantum computing configuration service may provide a solution (YES) ordetermine that a new configuration is required (NO).

In one example, the requested set of quantum computing services may notexist in the quantum set library 3908. In this example, one or more newquantum instances must be developed (trained) with the intelligenceservice 3834 using available data. In embodiments, alternateconfigurations may be developed with assistance from the intelligenceservice 3834 to identify alternate ways to provide all or some of therequested quantum computing services until appropriate resources becomeavailable. For example, a quantum/traditional hybrid model may bepossible that provides the requested service, but at a slower rate.

In embodiments, alternate configurations may be developed withassistance from the intelligence service 3834 to identify alternate andpossibly temporary ways to provide all or some of the requested quantumcomputing services. For example, a hybrid quantum/traditional model maybe possible that provides the requested service, but at a slower rate.This may also include a feedback learning loop to adjust services inreal time or to improved stored library elements.

When a quantum computing configuration has been identified andavailable, it is allocated and programmed for execution and delivery ofone or more quantum states (solutions).

Biology-Based Systems, Methods, Kits, and Apparatuses

FIGS. 40 and 41 together show a thalamus service 4000 and a set of inputsensors streaming data from various sources across a system 4002 withits centrally-managed data sources 4004. The thalamus service 4000filters the into the control system 4002 such that the control system isnever overwhelmed by the total volume of information. In embodiments,the thalamus service 4000 provides an information suppression mechanismfor information flows within the system. This mechanism monitors alldata streams and strips away irrelevant data streams by ensuring thatthe maximum data flows from all input sensors are always constrained.

The thalamus service 4000 may be a gateway for all communication thatresponds to the prioritization of the control system 4002. The controlsystem 4002 may decide to change the prioritization of the data streamedfrom the thalamus service 4000, for example, during a known fire in anisolated area, and the event may direct the thalamus service 4000 tocontinue to provide flame sensor information despite the fact thatmajority of this data is not unusual. The thalamus service 4000 may bean integral part of the overall system communication framework.

In embodiments, the thalamus service 4000 includes an intake managementsystem 4006. The intake management system 4006 may be configured toreceive and process multiple large datasets by converting them into datastreams that are sized and organized for subsequent use by a centralcontrol system 4002 operating within one or more systems. For example, arobot may include vision and sensing systems that are used by itscentral control system 4002 to identify and move through an environmentin real time. The intake management system 4006 can facilitate robotdecision-making by parsing, filtering, classifying, or otherwisereducing the size and increasing the utility of multiple large datasetsthat would otherwise overwhelm the central control system 4002. Inembodiments, the intake management system may include an intakecontroller 4008 that works with an intelligence service 4010 to evaluateincoming data and take actions-based evaluation results. Evaluations andactions may include specific instruction sets received by the thalamusservice 4000, for example the use of a set of specific compression andprioritization tools stipulated within a “Networking” library module. Inanother example, thalamus service inputs may direct the use of specificfiltering and suppression techniques. In a third example, thalamusservice inputs may stipulate data filtering associated with an area ofinterest such as a certain type of financial transaction. The intakemanagement system is also configured to recognize and manage datasetsthat are in a vectorized format such as PCMP, where they may be passeddirectly to central control, or alternatively deconstructed andprocessed separately. The intake management system 4006 may include alearning module that receives data from external sources that enablesimprovement and creation of application and data management librarymodules. In some cases, the intake management system may requestexternal data to augment existing datasets.

In embodiments, the control system 4002 may direct the thalamus service4000 to alter its filtering to provide more input from a set of specificsources. This indication more input is handled by the thalamus service4000 by suppressing other information flows based to constrain the totaldata flows to within a volume the central control system can handle.

The thalamus service 4000 can operate by suppressing data based onseveral different factors, and in embodiments, the default factor maybeunusualness of the data. This unusualness is a constant monitoring ofall input sensors and determining the unusualness of the data.

In some embodiments, the thalamus service 4000 may suppress data basedon geospatial factors. The thalamus service 4000 may be aware of thegeospatial location of all sensors and is able to look for unusualpatterns in data based on geospatial context and suppress dataaccordingly.

In some embodiments, the thalamus service 4000 may suppress data basedon temporal factors. Data can be suppressed temporally, for example, ifthe cadence of the data can be reduced such that the overall data streamis filtered to level that can be handled by the central processing unit.

In some embodiments, the thalamus service 4000 may suppress data basedon contextual factors. In embodiments, context-based filtering is afiltering event in which the thalamus service 4000 is aware of somecontext-based event. In this context the filtering is made to suppressinformation flows not relating to the data from the event.

In embodiments, the control system 4002 can override the thalamusfiltering and decide to focus on a completely different area for anyspecific reason.

In embodiments, the system may include a vector module. In embodiments,the vector module may be used to convert data to a vectorized format. Inmany examples, the conversion of a long sequence of oftentimes similarnumbers into a vector, which may include short term future predictions,makes the communication both smaller in size and forward looking innature. In embodiments, forecast methods may include: moving average;weighted moving average; Kalman filtering; exponential smoothing;autoregressive moving average (ARMA) (forecasts depend on past values ofthe variable being forecast, and on past prediction errors);autoregressive integrated moving average (ARIMA) (ARMA on theperiod-to-period change in the forecasted variable); extrapolation;linear prediction; trend estimation (predicting the variable as a linearor polynomial function of time); growth curve (e.g., statistics); andrecurrent neural network.

In embodiments, the system may include a predictive model communicationprotocol (PMCP) system to support vector-based predictive models and apredictive model communication protocol (PMCP). Under the PMCP protocol,instead of traditional streams where individual data items aretransmitted, vectors representing how the data is changing or what isthe forecast trend in the data is communicated. The PMCP system maytransmit actual model parameters and receiving units such that edgedevices can apply the vector-based predictive models to determine futurestates. For example, each automated device in a network could train aregression model or a neural network, constantly fitting the datastreams to current input data. All automated devices leveraging the PMCPsystem would be able to react in advance of events actually happening,rather than waiting for depletion of inventory for an item, for example,to occur. Continuing the example, the stateless automated device canreact to the forecast future state and make the necessary adjustments,such as ordering more of the item.

In embodiments, the PMCP system enables communicating vectorizedinformation and algorithms that allow vectorized information to beprocessed to refine the known information regarding a set ofprobability-based states. For example, the PMCP system may supportcommunicating the vectorized information gathered at each point of asensor reading but also adding algorithms that allow the information tobe processed. Applied in an environment with large numbers of sensorswith different accuracies and reliabilities, the probabilisticvector-based mechanism of the PMCP system allows large numbers, if notall, data streams to combine to produce refined models representing thecurrent state, past states and likely future states of goods.Approximation methods may include importance sampling, and the resultingalgorithm is known as a particle filter, condensation algorithm, orMonte Carlo localization.

In embodiments, the vector-based communication of the PMCP system allowsfuture security events to be anticipated, for example, by simple edgenode devices that are running in a semi-autonomous way. The edge devicesmay be responsible for building a set of forecast models showing trendsin the data. The parameters of this set of forecast models may betransmitted using the PMCP system.

Security systems are constantly looking for vectors showing change instate, as unusual events tend to trigger multiple vectors to showunusual patterns. In a security setting, seeing multiple simultaneousunusual vectors may trigger escalation and a response by, for example,the control system. In addition, one of the major areas of communicationsecurity concern is around the protection of stored data, and in avector-based system data does not need to be stored, and so the risk ofdata loss is simply removed.

In embodiments, PMCP data can be directly stored in a queryable databasewhere the actual data is reconstructed dynamically in response to aquery. In some embodiments, the PMCP data streams can be used torecreate the fine-grained data so they become part of an ExtractTransform and Load (ETL) process.

In embodiments where there are edge devices with very limitedcapacities, additional edge communication devices can be added toconvert the data into PMCP format. For example, to protect distributedmedical equipment from hacking attempts many manufacturers will chooseto not connect the device to any kind of network. To overcome thislimitation, the medical equipment may be monitored using sensors, suchas cameras, sound monitors, voltage detectors for power usage, chemicalsniffers, and the like. Functional unit learning and other datatechniques may be used to determine the actual usage of the medicalequipment detached from the network functional unit.

Communication using vectorized data allows for a constant view of likelyfuture states. This allows the future state to be communicated, allowingvarious entities to respond ahead of future state requirements withoutneeding access to the fine-grained data.

In embodiments, the PMCP protocol can be used to communicate relevantinformation about production levels and future trends in production.This PMCP data feed, with its built-in data obfuscation allows realcontextual information about production levels to be shared withconsumers, regulators, and other entities without requiring sensitivedata to be shared. For example, when choosing to purchase a new car, ifthere is an upcoming shortage of red paint then the consumer could beencouraged to choose a different color in order to maintain a desireddelivery time. PMCP and vector data enables simple data informedinteractive systems that user can apply without having to buildenormously complex big data engines. As an example, an upstreammanufacturer has an enormously complex task of coordinating manydownstream consumption points. Through the use of PMCP, the manufactureris able to provide real information to consumers without the need tostore detailed data and build complex models.

In embodiments, edge device units may communicate via the PMCP system toshow direction of movement and likely future positions. For example, amoving robot can communicate its likely track of future movement.

In embodiments, the PMCP system enables visual representations ofvector-based data (e.g., via a user interface), highlighting of areas ofconcern without the need to process enormous volumes of data. Therepresentation allows for the display of many monitored vector inputs.The user interface can then display information relating to the keyitems of interest, specifically vectors showing areas of unusual ortroublesome movement. This mechanism allows sophisticated models thatare built at the edge device edge nodes to feed into end usercommunications in a visually informative way.

Functional units produce a constant stream of “boring” data. By changingfrom producing data, to being monitored for problems, issues with thelogistical modules are highlighted without the need for scrutiny offine-grained data. In embodiments, the vectorizing process couldconstantly manage a predictive model showing future state. In thecontext of maintenance, these changes to the parameters in thepredictive model are in and of themselves predictors of change inoperational parameters, potentially indicating the need for maintenance.In embodiments, functional areas are not always designed to beconnected, but by allowing for an external device to virtually monitordevices, functional areas that do not allow for connectivity can becomepart of the information flow in the goods. This concept extends to allowfunctional areas that have limited connectivity to be monitoredeffectively by embellishing their data streams with vectorized monitoredinformation. Placing an automated device in the proximity of thefunctional unit that has limited or no connectivity allows capture ofinformation from the devices without the requirement of connectivity.There is also potential to add training data capture functional unitsfor these unconnected or limitedly connected functional areas. Thesetraining data capture functional units are typically quite expensive andcan provide high quality monitoring data, which is used as an input intothe proximity edge device monitoring device to provide data forsupervised learning algorithms.

Oftentimes, locations are laden with electrical interference, causingfundamental challenges with communications. The traditional approach ofstreaming all the fine-grained data is dependent on the completeness ofthe data stream. For example, if an edge device was to go offline for 10minutes, the streaming data and its information would be lost. Withvectorized communication, the offline unit continues to refine thepredictive model until the moment when it reconnects, which allows theupdated model to be transmitted via the PMCP system.

In embodiments, systems and devices may be based on the PMCP protocol.For example, cameras and vision systems (e.g., liquid lens systems),user devices, sensors, robots, smart containers, and the like may usePMCP and/or vector-based communication. By using vector-based cameras,for example, only information relating to the movement of items istransmitted. This reduces the data volume and by its nature filtersinformation about static items, showing only the changes in the imagesand focusing the data communication on elements of change. The overallshift in communication to communication of change is similar to how thehuman process of sight functions, where stationary items are not evencommunicated to the higher levels of the brain.

Radio Frequency Identification allows for massive volumes of mobile tagsto be tracked in real-time. In embodiments, the movement of the tags maybe communicated as vector information via the PMCP protocol, as thisform of communication is naturally suited to handing informationregarding the location of tag within the goods. Adding the ability toshow future state of the location using predictive models that can usepaths of prior movement allows the goods to change the fundamentalcommunication mechanism to one where units consuming data streams areconsuming information about the likely future state of the goods. Inembodiments, each tagged item may be represented as a probability-basedlocation matrix showing the likely probability of the tagged item beingat a position in space. The communication of movement shows thetransformation of the location probability matrix to a new set ofprobabilities. This probabilistic locational overview provides forconstant modeling of areas of likely intersection of moving units andallows for refinement of the probabilistic view of the location ofitems. Moving to a vector-based probability matrix allows units toconstantly handle the inherent uncertainty in the measurement of statusof various items, entities, and the like. In embodiments, statusincludes, but is not limited to, location, temperature, movement andpower consumption.

In embodiments, continuous connectivity is not required for continuousmonitoring of sensor inputs in a PMCP-based communication system. Forexample, a mobile robotic device with a plurality of sensors willcontinue to build models and predictions of data streams whiledisconnected from the network, and upon reconnection, the updated modelsare communicated. Furthermore, other systems or devices that use inputfrom the monitored system or device can apply the best known, typicallylast communicated, vector predictions to continue to maintain aprobabilistic understanding of the states of the goods.

CONCLUSION

The background description is presented simply for context, and is notnecessarily well-understood, routine, or conventional. Further, thebackground description is not an admission of what does or does notqualify as prior art. In fact, some or all of the background descriptionmay be work attributable to the named inventors that is otherwiseunknown in the art.

Certain operations described herein include interpreting, receiving,and/or determining one or more values, parameters, inputs, data, orother information (“receiving data”). Operations to receive datainclude, without limitation: receiving data via a user input; receivingdata over a network of any type; reading a data value from a memorylocation in communication with the receiving device; utilizing a defaultvalue as a received data value; estimating, calculating, or deriving adata value based on other information available to the receiving device;and/or updating any of these in response to a later received data value.In certain embodiments, a data value may be received by a firstoperation, and later updated by a second operation, as part of thereceiving a data value. For example, when communications are down,intermittent, or interrupted, a first receiving operation may beperformed, and when communications are restored an updated receivingoperation may be performed.

Certain logical groupings of operations herein, for example methods orprocedures of the current disclosure, are provided to illustrate aspectsof the present disclosure. Operations described herein are schematicallydescribed and/or depicted, and operations may be combined, divided,re-ordered, added, or removed in a manner consistent with the disclosureherein. It is understood that the context of an operational descriptionmay require an ordering for one or more operations, and/or an order forone or more operations may be explicitly disclosed, but the order ofoperations should be understood broadly, where any equivalent groupingof operations to provide an equivalent outcome of operations isspecifically contemplated herein. For example, if a value is used in oneoperational step, the determining of the value may be required beforethat operational step in certain contexts (e.g., where the time delay ofdata for an operation to achieve a certain effect is important), but maynot be required before that operation step in other contexts (e.g.,where usage of the value from a previous execution cycle of theoperations would be sufficient for those purposes). Accordingly, incertain embodiments an order of operations and grouping of operations asdescribed is explicitly contemplated herein, and in certain embodimentsre-ordering, subdivision, and/or different grouping of operations isexplicitly contemplated herein.

Physical (such as spatial and/or electrical) and functionalrelationships between elements (for example, between modules, circuitelements, semiconductor layers, etc.) are described using various terms.Unless explicitly described as being “direct,” when a relationshipbetween first and second elements is described, that relationshipencompasses both (i) a direct relationship where no other interveningelements are present between the first and second elements and (ii) anindirect relationship where one or more intervening elements are presentbetween the first and second elements.

Example relationship terms include “adjoining,” “transmitting,”“receiving,” “connected,” “engaged,” “coupled,” “adjacent,” “next to,”“on top of,” “above,” “below,” “abutting,” and “disposed.”

The detailed description includes specific examples for illustrationonly, and not to limit the disclosure or its applicability. The examplesare not intended to be an exhaustive list, but instead simplydemonstrate possession by the inventors of the full scope of thecurrently presented and envisioned future claims. Variations,combinations, and equivalents of the examples are within the scope ofthe disclosure.

No language in the specification should be construed as indicating thatany non-claimed element is essential or critical to the practice of thedisclosure.

The term “exemplary” simply means “example” and does not indicate a bestor preferred example.

The term “set” does not necessarily exclude the empty set—in otherwords, in some circumstances a “set” may have zero elements. The term“non-empty set” may be used to indicate exclusion of the empty set—thatis, a non-empty set must have one or more elements.

The term “subset” does not necessarily require a proper subset. In otherwords, a “subset” of a first set may be coextensive with (equal to) thefirst set. Further, the term “subset” does not necessarily exclude theempty set—in some circumstances a “subset” may have zero elements.

The phrase “at least one of A, B, and C” should be construed to mean alogical (A OR B OR C), using a non-exclusive logical OR, and should notbe construed to mean “at least one of A, at least one of B, and at leastone of C.”

The use of the terms “a,” “an,” “the,” and similar referents in thecontext of describing the disclosure and claims encompasses both thesingular and the plural, unless contradicted explicitly or by context.

Unless otherwise specified, the terms “comprising,” “having,” “with,”“including,” and “containing,” and their variants, are open-ended terms,meaning “including, but not limited to.”

Each publication referenced in this disclosure, including foreign anddomestic patent applications and patents, is hereby incorporated byreference in its entirety.

Although each of the embodiments is described above as having certainfeatures, any one or more of those features described with respect toany embodiment of the disclosure can be implemented in and/or combinedwith features of any of the other embodiments, even if that combinationis not explicitly described. In other words, the described embodimentsare not mutually exclusive, and permutations of multiple embodimentsremain within the scope of this disclosure.

One or more elements (for example, steps within a method, instructions,actions, or operations) may be executed in a different order (and/orconcurrently) without altering the principles of the present disclosure.

Unless technically infeasible, elements described as being in series maybe implemented partially or fully in parallel. Similarly, unlesstechnically infeasible, elements described as being in parallel may beimplemented partially or fully in series.

While the disclosure describes structures corresponding to claimedelements, those elements do not necessarily invoke a means plus functioninterpretation unless they explicitly use the signifier “means for.”

While the drawings divide elements of the disclosure into differentfunctional blocks or action blocks, these divisions are for illustrationonly. According to the principles of the present disclosure,functionality can be combined in other ways such that some or allfunctionality from multiple, separately depicted blocks can beimplemented in a single functional block; similarly, functionalitydepicted in a single block may be separated into multiple blocks.

Unless explicitly stated as mutually exclusive, features depicted indifferent drawings can be combined consistent with the principles of thepresent disclosure.

In the drawings, reference numbers may be reused to identify identicalelements or may simply identify elements that implement similarfunctionality.

Numbering or other labeling of instructions or method steps is done forconvenient reference, not to indicate a fixed order.

In the drawings, the direction of an arrow, as indicated by thearrowhead, generally demonstrates the flow of information (such as dataor instructions) that is of interest to the illustration. For example,when element A and element B exchange a variety of information, butinformation transmitted from element A to element B is relevant to theillustration, the arrow may point from element A to element B. Thisunidirectional arrow does not imply that no other information istransmitted from element B to element A. As just one example, forinformation sent from element A to element B, element B may sendrequests and/or acknowledgements to element A.

Unless otherwise indicated, recitations of ranges of values are merelyintended to serve as a shorthand way of referring individually to eachseparate value falling within the range, and each separate value ishereby incorporated into the specification as if it were individuallyrecited.

Special-Purpose Systems

A special-purpose system includes hardware and/or software and may bedescribed in terms of an apparatus, a method, or a computer-readablemedium. In various embodiments, functionality may be apportioneddifferently between software and hardware. For example, somefunctionality may be implemented by hardware in one embodiment and bysoftware in another embodiment. Further, software may be encoded byhardware structures, and hardware may be defined by software, such as insoftware-defined networking or software-defined radio.

In this application, including the claims, the term module refers to aspecial-purpose system. The module may be implemented by one or morespecial-purpose systems. The one or more special-purpose systems mayalso implement some or all of the other modules.

In this application, including the claims, the term “module” may bereplaced with the terms “controller” or “circuit.”

In this application, including the claims, the term platform refers toone or more modules that offer a set of functions.

In this application, including the claims, the term system may be usedinterchangeably with module or with the term special-purpose system.

The special-purpose system may be directed or controlled by an operator.The special-purpose system may be hosted by one or more of assets ownedby the operator, assets leased by the operator, and third-party assets.The assets may be referred to as a private, community, or hybrid cloudcomputing network or cloud computing environment.

For example, the special-purpose system may be partially or fully hostedby a third-party offering software as a service (SaaS), platform as aservice (PaaS), and/or infrastructure as a service (IaaS).

The special-purpose system may be implemented using agile developmentand operations (DevOps) principles. In embodiments, some or all of thespecial-purpose system may be implemented in a multiple-environmentarchitecture. For example, the multiple environments may include one ormore production environments, one or more integration environments, oneor more development environments, etc.

Device Examples

A special-purpose system may be partially or fully implemented using orby a mobile device. Examples of mobile devices include navigationdevices, cell phones, smart phones, mobile phones, mobile personaldigital assistants, palmtops, netbooks, pagers, electronic book readers,tablets, music players, etc.

A special-purpose system may be partially or fully implemented using orby a network device. Examples of network devices include switches,routers, firewalls, gateways, hubs, base stations, access points,repeaters, head-ends, user equipment, cell sites, antennas, towers, etc.

A special-purpose system may be partially or fully implemented using acomputer having a variety of form factors and other characteristics. Forexample, the computer may be characterized as a personal computer, as aserver, etc. The computer may be portable, as in the case of a laptop,netbook, etc. The computer may or may not have any output device, suchas a monitor, line printer, liquid crystal display (LCD), light emittingdiodes (LEDs), etc. The computer may or may not have any input device,such as a keyboard, mouse, touchpad, trackpad, computer vision system,barcode scanner, button array, etc. The computer may run ageneral-purpose operating system, such as the WINDOWS operating systemfrom Microsoft Corporation, the MACOS operating system from Apple, Inc.,or a variant of the LINUX operating system.

Examples of servers include a file server, print server, domain server,internet server, intranet server, cloud server,infrastructure-as-a-service server, platform-as-a-service server, webserver, secondary server, host server, distributed server, failoverserver, and backup server.

Hardware

The term “hardware” encompasses components such as processing hardware,storage hardware, networking hardware, and other general-purpose andspecial-purpose components. Note that these are not mutually exclusivecategories. For example, processing hardware may integrate storagehardware and vice versa.

Examples of a component are integrated circuits (ICs), applicationspecific integrated circuit (ASICs), digital circuit elements, analogcircuit elements, combinational logic circuits, gate arrays such asfield programmable gate arrays (FPGAs), digital signal processors(DSPs), complex programmable logic devices (CPLDs), etc.

Multiple components of the hardware may be integrated, such as on asingle die, in a single package, or on a single printed circuit board orlogic board. For example, multiple components of the hardware may beimplemented as a system-on-chip. A component, or a set of integratedcomponents, may be referred to as a chip, chipset, chiplet, or chipstack.

Examples of a system-on-chip include a radio frequency (RF)system-on-chip, an artificial intelligence (AI) system-on-chip, a videoprocessing system-on-chip, an organ-on-chip, a quantum algorithmsystem-on-chip, etc.

The hardware may integrate and/or receive signals from sensors. Thesensors may allow observation and measurement of conditions includingtemperature, pressure, wear, light, humidity, deformation, expansion,contraction, deflection, bending, stress, strain, load-bearing,shrinkage, power, energy, mass, location, temperature, humidity,pressure, viscosity, liquid flow, chemical/gas presence, sound, and airquality. A sensor may include image and/or video capture in visibleand/or non-visible (such as thermal) wavelengths, such as acharge-coupled device (CCD) or complementary metal-oxide semiconductor(CMOS) sensor.

Processing Hardware

Examples of processing hardware include a central processing unit (CPU),a graphics processing unit (GPU), an approximate computing processor, aquantum computing processor, a parallel computing processor, a neuralnetwork processor, a signal processor, a digital processor, a dataprocessor, an embedded processor, a microprocessor, and a co-processor.The co-processor may provide additional processing functions and/oroptimizations, such as for speed or power consumption. Examples of aco-processor include a math co-processor, a graphics co-processor, acommunication co-processor, a video co-processor, and an artificialintelligence (AI) co-processor.

Processor Architecture

The processor may enable execution of multiple threads. These multiplethreads may correspond to different programs. In various embodiments, asingle program may be implemented as multiple threads by the programmeror may be decomposed into multiple threads by the processing hardware.The threads may be executed simultaneously to enhance the performance ofthe processor and to facilitate simultaneous operations of theapplication.

A processor may be implemented as a packaged semiconductor die. The dieincludes one or more processing cores and may include additionalfunctional blocks, such as cache. In various embodiments, the processormay be implemented by multiple dies, which may be combined in a singlepackage or packaged separately.

Networking Hardware

The networking hardware may include one or more interface circuits. Insome examples, the interface circuit(s) may implement wired or wirelessinterfaces that connect, directly or indirectly, to one or morenetworks. Examples of networks include a cellular network, a local areanetwork (LAN), a wireless personal area network (WPAN), a metropolitanarea network (MAN), and/or a wide area network (WAN). The networks mayinclude one or more of point-to-point and mesh technologies. Datatransmitted or received by the networking components may traverse thesame or different networks. Networks may be connected to each other overa WAN or point-to-point leased lines using technologies such asMultiprotocol Label Switching (MPLS) and virtual private networks(VPNs).

Examples of cellular networks include GSM, GPRS, 3G, 4G, 5G, LTE, andEVDO. The cellular network may be implemented using frequency divisionmultiple access (FDMA) network or code division multiple access (CDMA)network.

Examples of a LAN are Institute of Electrical and Electronics Engineers(IEEE) Standard 802.11-2020 (also known as the WIFI wireless networkingstandard) and IEEE Standard 802.3-2018 (also known as the ETHERNET wirednetworking standard).

Examples of a WPAN include IEEE Standard 802.15.4, including the ZIGBEEstandard from the ZigBee Alliance. Further examples of a WPAN includethe BLUETOOTH wireless networking standard, including Core Specificationversions 3.0, 4.0, 4.1, 4.2, 5.0, and 5.1 from the Bluetooth SpecialInterest Group (SIG).

A WAN may also be referred to as a distributed communications system(DCS). One example of a WAN is the internet.

Storage Hardware

Storage hardware is or includes a computer-readable medium. The termcomputer-readable medium, as used in this disclosure, encompasses bothnonvolatile storage and volatile storage, such as dynamic random-accessmemory (DRAM). The term computer-readable medium only excludestransitory electrical or electromagnetic signals propagating through amedium (such as on a carrier wave). A computer-readable medium in thisdisclosure is therefore non-transitory and may also be consideredtangible.

EXAMPLES

Examples of storage implemented by the storage hardware include adatabase (such as a relational database or a NoSQL database), a datastore, a data lake, a column store, a data warehouse.

Example of storage hardware include nonvolatile memory devices, volatilememory devices, magnetic storage media, a storage area network (SAN),network-attached storage (NAS), optical storage media, printed media(such as bar codes and magnetic ink), and paper media (such as punchcards and paper tape). The storage hardware may include cache memory,which may be collocated with or integrated with processing hardware.

Storage hardware may have read-only, write-once, or read/writeproperties. Storage hardware may be random access or sequential access.Storage hardware may be location-addressable, file-addressable, and/orcontent-addressable.

Example of nonvolatile memory devices include flash memory (includingNAND and NOR technologies), solid state drives (SSDs), an erasableprogrammable read-only memory device such as an electrically erasableprogrammable read-only memory (EEPROM) device, and a mask read-onlymemory device (ROM).

Example of volatile memory devices include processor registers andrandom-access memory (RAM), such as static RAM (SRAM), dynamic RAM(DRAM), synchronous DRAM (SDRAM), synchronous graphics RAM (SGRAM), andvideo RAM (VRAM).

Example of magnetic storage media include analog magnetic tape, digitalmagnetic tape, and rotating hard disk drive (HDDs).

Examples of optical storage media include a CD (such as a CD-R, CD-RW,or CD-ROM), a DVD, a Blu-ray disc, and an Ultra HD Blu-ray disc.

Examples of storage implemented by the storage hardware include adistributed ledger, such as a permissioned or permissionless blockchain.

Entities recording transactions, such as in a blockchain, may reachconsensus using an algorithm such as proof-of-stake, proof-of-work, andproof-of-storage.

Elements of the present disclosure may be represented by or encoded asnon-fungible tokens (NFTs). Ownership rights related to the non-fungibletokens may be recorded in or referenced by a distributed ledger.

Transactions initiated by or relevant to the present disclosure may useone or both of fiat currency and cryptocurrencies, examples of whichinclude bitcoin and ether.

Some or all features of hardware may be defined using a language forhardware description, such as IEEE Standard 1364-2005 (commonly called“Verilog”) and IEEE Standard 1076-2008 (commonly called “VHDL”). Thehardware description language may be used to manufacture and/or programhardware.

A special-purpose system may be distributed across multiple differentsoftware and hardware entities. Communication within a special-purposesystem and between special-purpose systems may be performed usingnetworking hardware. The distribution may vary across embodiments andmay vary over time. For example, the distribution may vary based ondemand, with additional hardware and/or software entities invoked tohandle higher demand. In various embodiments, a load balancer may directrequests to one of multiple instantiations of the special purposesystem. The hardware and/or software entities may be physically distinctand/or may share some hardware and/or software, such as in a virtualizedenvironment. Multiple hardware entities may be referred to as a serverrack, server farm, data center, etc.

Software

Software includes instructions that are machine-readable and/orexecutable. Instructions may be logically grouped into programs, codes,methods, steps, actions, routines, functions, libraries, objects,classes, etc. Software may be stored by storage hardware or encoded inother hardware. Software encompasses (i) descriptive text to be parsed,such as HTML (hypertext markup language), XML (extensible markuplanguage), and JSON (JavaScript Object Notation), (ii) assembly code,(iii) object code generated from source code by a compiler, (iv) sourcecode for execution by an interpreter, (v) bytecode, (vi) source code forcompilation and execution by a just-in-time compiler, etc. As examplesonly, source code may be written using syntax from languages includingC, C++, JavaScript, Java, Python, R, etc.

Software also includes data. However, data and instructions are notmutually exclusive categories. In various embodiments, the instructionsmay be used as data in one or more operations. As another example,instructions may be derived from data.

The functional blocks and flowchart elements in this disclosure serve assoftware specifications, which can be translated into software by theroutine work of a skilled technician or programmer.

Software may include and/or rely on firmware, processor microcode, anoperating system (OS), a basic input/output system (BIOS), applicationprogramming interfaces (APIs), libraries such as dynamic-link libraries(DLLs), device drivers, hypervisors, user applications, backgroundservices, background applications, etc. Software includes nativeapplications and web applications. For example, a web application may beserved to a device through a browser using hypertext markup language 5threvision (HTML5).

Software may include artificial intelligence systems, which may includemachine learning or other computational intelligence. For example,artificial intelligence may include one or more models used for one ormore problem domains.

When presented with many data features, identification of a subset offeatures that are relevant to a problem domain may improve predictionaccuracy, reduce storage space, and increase processing speed. Thisidentification may be referred to as feature engineering. Featureengineering may be performed by users or may only be guided by users. Invarious implementations, a machine learning system may computationallyidentify relevant features, such as by performing singular valuedecomposition on the contributions of different features to outputs.

Examples of the models include recurrent neural networks (RNNs) such aslong short-term memory (LSTM), deep learning models such astransformers, decision trees, support-vector machines, geneticalgorithms, Bayesian networks, and regression analysis. Examples ofsystems based on a transformer model include bidirectional encoderrepresentations from transformers (BERT) and generative pre-trainedtransformer (GPT).

Training a machine-learning model may include supervised learning (forexample, based on labelled input data), unsupervised learning, andreinforcement learning. In various embodiments, a machine-learning modelmay be pre-trained by their operator or by a third party.

Problem domains include nearly any situation where structured data canbe collected, and includes natural language processing (NLP), computervision (CV), classification, image recognition, etc.

Architectures

Some or all of the software may run in a virtual environment rather thandirectly on hardware. The virtual environment may include a hypervisor,emulator, sandbox, container engine, etc. The software may be built as avirtual machine, a container, etc. Virtualized resources may becontrolled using, for example, a DOCKER™ container platform, a pivotalcloud foundry (PCF) platform, etc.

In a client-server model, some of the software executes on firsthardware identified functionally as a server, while other of thesoftware executes on second hardware identified functionally as aclient. The identity of the client and server is not fixed: for somefunctionality, the first hardware may act as the server while for otherfunctionality, the first hardware may act as the client. In differentembodiments and in different scenarios, functionality may be shiftedbetween the client and the server. In one dynamic example, somefunctionality normally performed by the second hardware is shifted tothe first hardware when the second hardware has less capability. Invarious embodiments, the term “local” may be used in place of “client,”and the term “remote” may be used in place of “server.”

Some or all of the software may be logically partitioned intomicroservices. Each microservice offers a reduced subset offunctionality. In various embodiments, each microservice may be scaledindependently depending on load, either by devoting more resources tothe microservice or by instantiating more instances of the microservice.In various embodiments, functionality offered by one or moremicroservices may be combined with each other and/or with other softwarenot adhering to a microservices model.

Some or all of the software may be arranged logically into layers. In alayered architecture, a second layer may be logically placed between afirst layer and a third layer. The first layer and the third layer wouldthen generally interact with the second layer and not with each other.In various embodiments, this is not strictly enforced—that is, somedirect communication may occur between the first and third layers.

1. An AI-based platform for enabling intelligent orchestration andmanagement of power and energy, comprising: a set of edge devicesincluding a set of artificial intelligence systems that are configuredto: process data handled by the edge devices; and determine, based onthe data, a mix of energy generation, storage, delivery and/orconsumption characteristics for a set of systems that are in localcommunication with the edge devices and to output a data set thatindicates constituent proportions of the mix.
 2. The AI-based platformof claim 1, wherein the output data set indicates a fraction of energygenerated by an energy grid and a fraction of energy generated by a setof distributed energy resources that operate independently of the energygrid.
 3. The AI-based platform of claim 1, wherein the output data setindicates a fraction of energy generated by renewable energy resourcesand a fraction of energy generated by nonrenewable resources.
 4. TheAI-based platform of claim 1, wherein the output data set indicates afraction of energy generation by type for each interval in a series oftime intervals.
 5. The AI-based platform of claim 1, wherein the outputdata set indicates carbon generation associated with energy generationfor each type of energy in the energy mix during each interval of aseries of time intervals.
 6. The AI-based platform of claim 1, whereinthe output data set indicates carbon emissions associated with energygeneration for each type of energy in the energy mix during eachinterval of a series of time intervals.
 7. The AI-based platform ofclaim 1, wherein at least one of the edge devices is further configuredto adapt a transport of data over a network and/or communication system,wherein the adapting is based on at least one of, a congestioncondition, a delay and/or latency condition, a packet loss condition, anerror rate condition, a cost of transport condition, aquality-of-service (QoS) condition, a usage condition, a market factorcondition, or a user configuration condition.
 8. The AI-based platformof claim 1, further comprising an adaptive energy digital twin thatrepresents at least one of, an energy stakeholder entity, an energydistribution resource, a stakeholder information technology, anetworking infrastructure entity, an energy-dependent stakeholderproduction facility, a stakeholder transportation system, a marketcondition, or an energy usage priority condition.
 9. The AI-basedplatform of claim 1, further comprising an adaptive energy digital twinthat is configured to perform at least one of, providing a visual and/oranalytic indicator of energy consumption by at least one energyconsumer, filtering energy data, highlighting energy data, or adjustingenergy data.
 10. The AI-based platform of claim 1, further comprising anadaptive energy digital twin that is configured to generate a visualand/or analytic indicator of energy consumption by at least one of, atleast one machine, at least one factory, or at least one vehicle in avehicle fleet.
 11. The AI-based platform of claim 1, wherein at leastone of the edge devices is further configured to perform at least oneof, extracting energy-related data, detecting and/or correcting errorsin energy-related data, transforming, converting, normalizing, and/orcleansing energy-related data, parsing energy-related data, detectingpatterns, content, and/or objects in energy-related data, compressingenergy-related data, streaming energy-related data, filteringenergy-related data, loading and/or storing energy-related data, routingand/or transporting energy-related data, or maintaining security ofenergy-related data.
 12. The AI-based platform of claim 1, wherein thedata is based on at least one public data resource, the public dataresources including at least one of, a weather data resource, asatellite data resource, a census, population, demographic, and/orpsychographic data resource, a market data resource, or an ecommercedata resource.
 13. The AI-based platform of claim 1, wherein the data isbased on at least one enterprise data resource, the enterprise dataresources including at least one of, resource planning data, salesand/or marketing data, financial planning data, demand planning data,supply chain data, procurement data, pricing data, customer data,product data, or operating data.
 14. The AI-based platform of claim 1,wherein at least one of the edge devices includes at least one AI-basedmodel and/or algorithm, the at least one AI-based model and/or algorithmis trained based on a training data set, and the training data set isbased on at least one of, at least one human tag and/or label, at leastone human interaction with a hardware and/or software system, at leastone outcome, at least one AI-generated training data sample, asupervised learning training process, a semi-supervised learningtraining process, or a deep learning training process.
 15. The AI-basedplatform of claim 1, wherein at least one of the edge devices is furtherconfigured to orchestrate delivery of energy to at least one point ofconsumption, and the delivery of the energy includes at least one of, atleast one fixed transmission line, at least one instance of wirelessenergy transmission, at least one delivery of fuel, or at least onedelivery of stored energy.
 16. The AI-based platform of claim 1, whereinat least one of the edge devices is further configured to record, in adistributed ledger and/or blockchain, at least one energy-related event,the at least one energy-related event including at least one of, anenergy purchase and/or sale event, a service charge associated with anenergy purchase and/or sale event, an energy consumption event, anenergy generation event, an energy distribution event, an energy storageevent, a carbon emission production event, a carbon emission abatementevent, a renewable energy credit event, a pollution production event, ora pollution abatement event.
 17. The AI-based platform of claim 1,wherein at least one of the edge devices is deployed in an off-gridenvironment, and the off-grid environment includes at least one of, anoff-grid energy generation system, an off-grid energy storage system, oran off-grid energy mobilization system.
 18. The AI-based platform ofclaim 1, wherein at least a portion of the set of edge devices islocated in proximity to at least one entity that generates, stores,delivers, and/or uses energy.
 19. The AI-based platform of claim 1,wherein the set of edge devices provides information about an energystate and/or energy flow of at least one entity that generates, stores,delivers, and/or uses energy.
 20. The AI-based platform of claim 1,wherein the set of edge devices contains and/or governs at least onesensor of a set of sensors, and the set of sensors is associated with aset of infrastructure assets that are configured to generate, store,deliver, and/or use energy.